#Text_mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality #information from #text.
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@text_mining tasks include #text_categorization, #text_clustering, #concept_extraction, production of #granular_taxonomies, #sentiment_analysis, #document_summarization
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@sentiment_analysis #wordnet #conceptnet
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#text_mining #nlp
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#lsa is a technique in #nlp
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#LSA assumes that #words that are close in meaning will occur in similar pieces of #text
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#lsa can use a #term_document_matrix which describes the occurrences of #terms in #documents; it is a #sparse_matrix whose rows correspond to #terms and whose columns correspond to #documents.
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A typical example of the weighting of the elements of the #matrix in #lsa is #tf_idf (term #frequency–inverse document #frequency): the weight of an element of the #matrix is proportional to the number of times the #terms appear in each document, where rare #terms are upweighted to reflect their relative importance.
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#lsa can be used to #analyze #word_association in #text_corpus
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#lsa has been used to assist in performing #prior_art searches for #patents.
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The use of #lsa has been prevalent in the study of human #memory, especially in areas of#free_recall and #memory_search.
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#lsi is an #indexing and #retrieval #method
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#lsi is #lsa
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explicit semantic analysis (@esa) is a #vectoral_representation of #text (individual words or entire #documents) that uses a #document_corpus as a knowledge base. Specifically, in esa, a word is represented as a column vector in the #tf_idf matrix of the #text corpus and a #document (string of words) is represented as the centroid of the vectors representing its words.
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#document_corpus #document
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#documents #document
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@topic_model is a type of #statistical_model for discovering the abstract "#topics" that occur in a collection of #documents.
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An early #topic_model was described by Papadimitriou, Raghavan, Tamaki and Vempala in 1998.[2] Another one, called probabilistic latent semantic analysis (#plsa), was created by Thomas Hofmann in 1999.[3] Latent Dirichlet allocation (#lda), perhaps the most common #topic_model currently in use, is a generalization of #plsa
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#plsa #lsa
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#lda introduces sparse #dirichlet prior distributions over #document_topic and #topic_word distributions, encoding the intuition that documents cover a small number of topics and that topics often use a small number of words.[
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#topic_models are generally extensions on #lda, such as Pachinko allocation, which improves on #lda by modeling #correlations between #topics in addition to the #word #correlations which constitute #topics
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#tf_idf or #tfidf, short for term #frequency–inverse #document #frequency, is a numerical #statistic that is intended to reflect how important a #word is to a #document in a collection or #corpus.
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83% of text-based #recommender systems in digital libraries use #tf_idf
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latent Dirichlet allocation (#lda) is a generative statistical #model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.
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In #lda, each #document may be viewed as a mixture of various #topics where each #document is considered to have a set of #topics that are assigned to it via #lda. This is identical to probabilistic latent semantic analysis (#plsa), except that in #lda the #topic distribution is assumed to have a sparse #dirichlet prior.
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#lda is a generalization of the #plsa model, which is equivalent to #lda under a uniform #dirichlet prior #distribution.
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#lda and #lsa study commonalities between different #words (how often they are used together) to identify #topics that go together. this data can then be used to extract a right set of #documents for a #search #query or to see which #words are related to a #search #query
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pachinko allocation model (#pam) is a #topic model. Topic #models are a suite of algorithms to uncover the hidden thematic structure of a #collection of #documents. [1] The algorithm improves upon earlier #topic #models such as latent Dirichlet allocation (#lda) by modeling #correlations between #topics in addition to the #word #correlations which constitute #topics.
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#dirichlet process is a #probability #distribution whose range is itself a set of #probability #distribution
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#n_gram is a contiguous #sequence of n items from a given sample of #text or #speech.
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An #n_gram #model is a type of probabilistic language #model for predicting the next item in such a #sequence in the form of a (n − 1)–order #markov #model
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#rst in #text #summarization and other applications. #rst addresses #text organization by means of #relations that hold between parts of #text. It explains coherence by postulating a hierarchical, connected #structure of #texts
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#rhetorical_structure_theory #rst
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#semantic_folding theory describes a procedure for encoding the #semantics of natural #language #text in a semantically grounded #binary #representation.
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#semantic_compression is a process of compacting a #lexicon used to build a textual #document
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#tf_idf with #k_means #clustering,
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#k_means returns #cluster centroids as "#topics" and #lda assigns #words to the different #topics
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#topic_modeling through 4 of the most popular techniques today: #lsa, #plsa, #lda, and the newer, #deep_learning based #lda2vec. https://medium.com/nanonets/topic-modeling-with-lsa-psla-lda-and-#lda2vec-555ff65b0b05
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for #lsa we need a #corpus of #documents to analyze which #words belong to which #documents and to also weigh out the ones that are too frequently appearing in all the #texts
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This #dimensionality_reduction can be performed using truncated #svd so that #topic #document #matrix is converted into #term #topic #matrix
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#lda is a #bayesian version of #plsa.
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At the #word level, we typically use something like #word2vec to obtain #vector representations.
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#lda2vec is an extension of #word2vec and #lda that jointly learns #word, #document, and #topic_vectors
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Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into
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a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).
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Text analysis involves information retrieval, lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization, and predictive analytics. The overarching goal is, essentially, to turn text into data for analysis, via application of natural language processing (NLP) and analytical methods.
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A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted.
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The term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation.[1] The term is roughly synonymous with text mining; indeed, Ronen Feldman modified a 2000 description of "text mining"[2] in 2004 to describe "text analytics".[3] The latter term is now used more frequently in business settings while "text mining"
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is used in some of the earliest application areas, dating to the 1980s,[4] notably life-sciences research and government intelligence.
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The term text analytics also describes that application of text analytics to respond to business problems, whether independently or in conjunction with query and analysis of fielded, numerical data. It is a truism that 80 percent of business-relevant information originates in unstructured form, primarily text.[5] These techniques and processes discover and present knowledge – facts, business rules, and relationships – that is otherwise locked in textual form, impenetrable to automated
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processing.
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Increasing interest is being paid to multilingual data mining: the ability to gain information across languages and cluster similar items from different linguistic sources according to their meaning.
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The challenge of exploiting the large proportion of enterprise information that originates in "unstructured" form has been recognized for decades.[6] It is recognized in the earliest definition of business intelligence (BI), in an October 1958 IBM Journal article by H. P. Luhn, A Business Intelligence System, which describes a system that will:
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"...utilize data-processing machines for auto-abstracting and auto-encoding of documents and for creating interest profiles for each of the 'action points' in an organization. Both incoming and internally generated documents are automatically abstracted, characterized by a word pattern, and sent automatically to appropriate action points."
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Yet as management information systems developed starting in the 1960s, and as BI emerged in the '80s and '90s as a software category and field of practice, the emphasis was on numerical data stored in relational databases. This is not surprising: text in "unstructured" documents is hard to process. The emergence of text analytics in its current form stems from a refocusing of research in the late 1990s from algorithm development to application, as described by Prof. Marti A. Hearst in the paper
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Untangling Text Data Mining:[7]
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For almost a decade the computational linguistics community has viewed large text collections as a resource to be tapped in order to produce better text analysis algorithms. In this paper, I have attempted to suggest a new emphasis: the use of large online text collections to discover new facts and trends about the world itself. I suggest that to make progress we do not need fully artificial intelligent text analysis; rather, a mixture of computationally-driven and user-guided analysis may open
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the door to exciting new results.
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Hearst's 1999 statement of need fairly well describes the state of text analytics technology and practice a decade later.
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The technology is now broadly applied for a wide variety of government, research, and business needs. Applications can be sorted into a number of categories by analysis type or by business function. Using this approach to classifying solutions, application categories include:
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Many text mining software packages are marketed for security applications, especially monitoring and analysis of online plain text sources such as Internet news, blogs, etc. for national security purposes.[10] It is also involved in the study of text encryption/decryption.
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A range of text mining applications in the biomedical literature has been described.[11] e.g. Protein Docking [12]
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One online text mining application in the biomedical literature is PubGene that combines biomedical text mining with network visualization as an Internet service.[13][14]
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Text mining methods and software is also being researched and developed by major firms, including IBM and Microsoft, to further automate the mining and analysis processes, and by different firms working in the area of search and indexing in general as a way to improve their results. Within public sector much effort has been concentrated on creating software for tracking and monitoring terrorist activities.[15]
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Text mining is being used by large media companies, such as the Tribune Company, to clarify information and to provide readers with greater search experiences, which in turn increases site "stickiness" and revenue. Additionally, on the back end, editors are benefiting by being able to share, associate and package news across properties, significantly increasing opportunities to monetize content.
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Text mining is starting to be used in marketing as well, more specifically in analytical customer relationship management.[16] Coussement and Van den Poel (2008)[17][18] apply it to improve predictive analytics models for customer churn (customer attrition).[17] Text mining is also being applied in stock returns prediction.[19]
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Sentiment analysis may involve analysis of movie reviews for estimating how favorable a review is for a movie.[20]
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Such an analysis may need a labeled data set or labeling of the affectivity of words. Resources for affectivity of words and concepts have been made for WordNet[21] and ConceptNet,[22] respectively.
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Text has been used to detect emotions in the related area of affective computing.[23] Text based approaches to affective computing have been used on multiple corpora such as students evaluations, children stories and news stories.
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The issue of text mining is of importance to publishers who hold large databases of information needing indexing for retrieval. This is especially true in scientific disciplines, in which highly specific information is often contained within written text. Therefore, initiatives have been taken such as Nature's proposal for an Open Text Mining Interface (OTMI) and the National Institutes of Health's common Journal Publishing Document Type Definition (DTD) that would provide semantic cues to
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machines to answer specific queries contained within text without removing publisher barriers to public access.
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Academic institutions have also become involved in the text mining initiative:
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The automatic analysis of vast textual corpora has created the possibility for scholars to analyse millions of documents in multiple languages with very limited manual intervention. Key enabling technologies have been parsing, machine translation, topic categorization, and machine learning.
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The automatic parsing of textual corpora has enabled the extraction of actors and their relational networks on a vast scale, turning textual data into network data. The resulting networks, which can contain thousands of nodes, are then analysed by using tools from network theory to identify the key actors, the key communities or parties, and general properties such as robustness or structural stability of the overall network, or centrality of certain nodes.[28] This automates the approach
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introduced by quantitative narrative analysis,[29] whereby subject-verb-object triplets are identified with pairs of actors linked by an action, or pairs formed by actor-object.[27]
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Content analysis has been a traditional part of social sciences and media studies for a long time. The automation of content analysis has allowed a "big data" revolution to take place in that field, with studies in social media and newspaper content that include millions of news items. Gender bias, readability, content similarity, reader preferences, and even mood have been analyzed based on text mining methods over millions of documents.[30][31][32][33][34] The analysis of readability, gender
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bias and topic bias was demonstrated in Flaounas et al.[35] showing how different topics have different gender biases and levels of readability; the possibility to detect mood patterns in a vast population by analysing Twitter content was demonstrated as well.[36][37]
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Text mining computer programs are available from many commercial and open source companies and sources. See List of text mining software.
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Because of a lack of flexibilities in European copyright and database law, the mining of in-copyright works (such as web mining) without the permission of the copyright owner is illegal. In the UK in 2014, on the recommendation of the Hargreaves review the government amended copyright law[38] to allow text mining as a limitation and exception. It was only the second country in the world to do so, following Japan, which introduced a mining-specific exception in 2009. However, owing to the
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restriction of the Copyright Directive, the UK exception only allows content mining for non-commercial purposes. UK copyright law does not allow this provision to be overridden by contractual terms and conditions.
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The European Commission facilitated stakeholder discussion on text and data mining in 2013, under the title of Licences for Europe.[39] The fact that the focus on the solution to this legal issue was licences, and not limitations and exceptions to copyright law, led representatives of universities, researchers, libraries, civil society groups and open access publishers to leave the stakeholder dialogue in May 2013.[40]
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By contrast to Europe, the flexible nature of US copyright law, and in particular fair use, means that text mining in America, as well as other fair use countries such as Israel, Taiwan and South Korea, is viewed as being legal. As text mining is transformative, meaning that it does not supplant the original work, it is viewed as being lawful under fair use. For example, as part of the Google Book settlement the presiding judge on the case ruled that Google's digitisation project of
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in-copyright books was lawful, in part because of the transformative uses that the digitisation project displayed—one such use being text and data mining.[41]
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Until recently, websites most often used text-based searches, which only found documents containing specific user-defined words or phrases. Now, through use of a semantic web, text mining can find content based on meaning and context (rather than just by a specific word). Additionally, text mining software can be used to build large dossiers of information about specific people and events. For example, large datasets based on data extracted from news reports can be built to facilitate social
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networks analysis or counter-intelligence. In effect, the text mining software may act in a capacity similar to an intelligence analyst or research librarian, albeit with a more limited scope of analysis. Text mining is also used in some email spam filters as a way of determining the characteristics of messages that are likely to be advertisements or other unwanted material. Text mining plays an important role in determining financial market sentiment. https://en.wikipedia.org/wiki/Text_mining
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Deconstruction is a critique of the relationship between text and meaning originated by the philosopher Jacques Derrida. Derrida's approach consisted in conducting readings of texts with an ear to what runs counter to the intended meaning or structural unity of a particular text. The purpose of deconstruction is to show that the usage of language in a given text, and language as a whole, are irreducibly complex, unstable, or impossible. Throughout his readings, Derrida hoped to show
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deconstruction at work.
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Many debates in continental philosophy surrounding ontology, epistemology, ethics, aesthetics, hermeneutics, and philosophy of language refer to Derrida's observations. Since the 1980s, these observations inspired a range of theoretical enterprises in the humanities,[1] including the disciplines of law[2]:3–76[3][4] anthropology,[5] historiography,[6] linguistics,[7] sociolinguistics,[8] psychoanalysis, LGBT studies, and the feminist school of thought. Deconstruction also inspired
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deconstructivism in architecture and remains important within art,[9] music,[10] and literary criticism.[11][12]
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While common in continental Europe (and wherever Continental philosophy is in the mainstream), deconstruction is not adopted or accepted by most philosophy departments in universities where analytic philosophy has the upper hand.
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Jacques Derrida's 1967 book Of Grammatology introduced the majority of ideas influential within deconstruction.[13]:25 Derrida published a number of other works directly relevant to the concept of deconstruction. Books showing deconstruction in action or defining it more completely include Différance, Speech and Phenomena, and Writing and Difference.
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According to Derrida and taking inspiration from the work of Ferdinand de Saussure,[14] language as a system of signs and words only has meaning because of the contrast between these signs.[15][13]:7, 12 As Rorty contends, "words have meaning only because of contrast-effects with other words...no word can acquire meaning in the way in which philosophers from Aristotle to Bertrand Russell have hoped it might—by being the unmediated expression of something non-linguistic (e.g., an emotion, a
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sense-datum, a physical object, an idea, a Platonic Form)".[15] As a consequence, meaning is never present, but rather is deferred to other signs. Derrida refers to the—in this view, mistaken—belief that there is a self-sufficient, non-deferred meaning as metaphysics of presence. A concept, then, must be understood in the context of its opposite, such as being/nothingness, normal/abnormal, speech/writing, etc.[16][17]:26
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Further, Derrida contends that "in a classical philosophical opposition we are not dealing with the peaceful coexistence of a vis-a-vis, but rather with a violent hierarchy. One of the two terms governs the other (axiologically, logically, etc.), or has the upper hand": signified over signifier; intelligible over sensible; speech over writing; activity over passivity, etc. The first task of deconstruction would be to find and overturn these oppositions inside a text or a corpus of texts; but
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the final objective of deconstruction is not to surpass all oppositions, because it is assumed they are structurally necessary to produce sense. The oppositions simply cannot be suspended once and for all. The hierarchy of dual oppositions always reestablishes itself. Deconstruction only points to the necessity of an unending analysis that can make explicit the decisions and arbitrary violence intrinsic to all texts.[17]:41
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Finally, Derrida argues that it is not enough to expose and deconstruct the way oppositions work and then stop there in a nihilistic or cynical position, "thereby preventing any means of intervening in the field effectively".[17]:42 To be effective, deconstruction needs to create new terms, not to synthesize the concepts in opposition, but to mark their difference and eternal interplay. This explains why Derrida always proposes new terms in his deconstruction, not as a free play but as a pure
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necessity of analysis, to better mark the intervals. Derrida called undecidables—that is, unities of simulacrum—"false" verbal properties (nominal or semantic) that can no longer be included within philosophical (binary) opposition, but which, however, inhabit philosophical oppositions—resisting and organizing it—without ever constituting a third term, without ever leaving room for a solution in the form of Hegelian dialectics (e.g., différance, archi-writing, pharmakon, supplement, hymen,
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gram, spacing).[17]:19
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Derrida's theories on deconstruction were themselves influenced by the work of linguists such as Ferdinand de Saussure (whose writings on semiotics also became a cornerstone of structuralist theory in the mid-20th century) and literary theorists such as Roland Barthes (whose works were an investigation of the logical ends of structuralist thought). Derrida's views on deconstruction stood in opposition to the theories of structuralists such as psychoanalytic theorist Jacques Lacan, and linguist
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Claude Lévi-Strauss. However, Derrida resisted attempts to label his work as "post-structuralist".[citation needed]
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In order to understand Derrida's motivation, one must refer to Nietzsche's philosophy.
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Nietzsche's project began with Orpheus, the man underground. This foil to Platonic light was deliberately and self-consciously lauded in Daybreak, when Nietzsche announces, albeit retrospectively, "In this work you will discover a subterranean man at work", and then goes on to map the project of unreason: "All things that live long are gradually so saturated with reason that their origin in unreason thereby becomes improbable. Does not almost every precise history of an origination impress our
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feelings as paradoxical and wantonly offensive? Does the good historian not, at bottom, constantly contradict?".[18][page needed]
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Nietzsche's point in Daybreak is that standing at the end of modern history, modern thinkers know too much to be deceived by the illusion of reason any more. Reason, logic, philosophy and science are no longer solely sufficient as the royal roads to truth. And so Nietzsche decides to throw it in our faces, and uncover the truth of Plato, that he—unlike Orpheus—just happened to discover his true love in the light instead of in the dark. This being merely one historical event amongst many,
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Nietzsche proposes that we revisualize the history of the West as the history of a series of political moves, that is, a manifestation of the will to power, that at bottom have no greater or lesser claim to truth in any noumenal (absolute) sense. By calling our attention to the fact that he has assumed the role of Orpheus, the man underground, in dialectical opposition to Plato, Nietzsche hopes to sensitize us to the political and cultural context, and the political influences that impact
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authorship. For example, the political influences that led one author to choose philosophy over poetry (or at least portray himself as having made such a choice), and another to make a different choice.
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The problem with Nietzsche, as Derrida sees it, is that he did not go far enough. That he missed the fact that this will to power is itself but a manifestation of the operation of writing. And so Derrida wishes to help us step beyond Nietzsche's penultimate revaluation of all western values, to the ultimate, which is the final appreciation of "the role of writing in the production of knowledge".[19]
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Derrida approaches all texts as constructed around elemental oppositions which all discourse has to articulate if it intends to make any sense whatsoever. This is so because identity is viewed in non-essentialist terms as a construct, and because constructs only produce meaning through the interplay of difference inside a "system of distinct signs". This approach to text is influenced by the semiology of Ferdinand de Saussure.[20][21]
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Saussure is considered one of the fathers of structuralism when he explained that terms get their meaning in reciprocal determination with other terms inside language:
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In language there are only differences. Even more important: a difference generally implies positive terms between which the difference is set up; but in language there are only differences without positive terms. Whether we take the signified or the signifier, language has neither ideas nor sounds that existed before the linguistic system, but only conceptual and phonic differences that have issued from the system. The idea or phonic substance that a sign contains is of less importance than
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the other signs that surround it. [...] A linguistic system is a series of differences of sound combined with a series of differences of ideas; but the pairing of a certain number of acoustical signs with as many cuts made from the mass thought engenders a system of values.[14]
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Saussure explicitly suggested that linguistics was only a branch of a more general semiology, a science of signs in general, human codes being only one part. Nevertheless, in the end, as Derrida pointed out, Saussure made linguistics "the regulatory model", and "for essential, and essentially metaphysical, reasons had to privilege speech, and everything that links the sign to phone".[17]:21, 46, 101, 156, 164 Derrida will prefer to follow the more "fruitful paths (formalization)" of a general
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semiotics without falling into what he considered "a hierarchizing teleology" privileging linguistics, and to speak of "mark" rather than of language, not as something restricted to mankind, but as prelinguistic, as the pure possibility of language, working everywhere there is a relation to something else.
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Derrida's original use of the word "deconstruction" was a translation of Destruktion, a concept from the work of Martin Heidegger that Derrida sought to apply to textual reading. Heidegger's term referred to a process of exploring the categories and concepts that tradition has imposed on a word, and the history behind them.[22]
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Derrida's concerns flow from a consideration of several issues:
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A desire to contribute to the re-evaluation of all Western values, a re-evaluation built on the 18th-century Kantian critique of reason, and carried forward to the 19th century, in its more radical implications, by Kierkegaard and Nietzsche.
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An assertion that texts outlive their authors, and become part of a set of cultural habits equal to, if not surpassing, the importance of authorial intent.
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A re-valuation of certain classic western dialectics: poetry vs. philosophy, reason vs. revelation, structure vs. creativity, episteme vs. techne, etc.
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To this end, Derrida follows a long line of modern philosophers, who look backwards to Plato and his influence on the Western metaphysical tradition.[19][page needed] Like Nietzsche, Derrida suspects Plato of dissimulation in the service of a political project, namely the education, through critical reflections, of a class of citizens more strategically positioned to influence the polis. However, like Nietzsche, Derrida is not satisfied merely with such a political interpretation of Plato,
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because of the particular dilemma modern humans find themselves in. His Platonic reflections are inseparably part of his critique of modernity, hence the attempt to be something beyond the modern, because of this Nietzschian sense that the modern has lost its way and become mired in nihilism.
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Différance is the observation that the meanings of words come from their synchrony with other words within the language and their diachrony between contemporary and historical definitions of a word. Understanding language, according to Derrida, requires an understanding of both viewpoints of linguistic analysis. The focus on diachrony has led to accusations against Derrida of engaging in the etymological fallacy.[23]
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There is one statement by Derrida—in an essay on Rousseau in Of Grammatology—which has been of great interest to his opponents.[13]:158 It is the assertion that "there is no outside-text" (il n'y a pas de hors-texte),[13]:158–59, 163 which is often mistranslated as "there is nothing outside of the text". The mistranslation is often used to suggest Derrida believes that nothing exists but words. Michel Foucault, for instance, famously misattributed to Derrida the very different phrase "Il n'y a
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rien en dehors du texte" for this purpose.[24] According to Derrida, his statement simply refers to the unavoidability of context that is at the heart of différance.[25]:133
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For example, the word "house" derives its meaning more as a function of how it differs from "shed", "mansion", "hotel", "building", etc. (Form of Content, that Louis Hjelmslev distinguished from Form of Expression) than how the word "house" may be tied to a certain image of a traditional house (i.e., the relationship between signified and signifier), with each term being established in reciprocal determination with the other terms than by an ostensive description or definition: when can we talk
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about a "house" or a "mansion" or a "shed"? The same can be said about verbs, in all the languages in the world: when should we stop saying "walk" and start saying "run"? The same happens, of course, with adjectives: when must we stop saying "yellow" and start saying "orange", or exchange "past" for "present"? Not only are the topological differences between the words relevant here, but the differentials between what is signified is also covered by différance.
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Thus, complete meaning is always "differential" and postponed in language; there is never a moment when meaning is complete and total. A simple example would consist of looking up a given word in a dictionary, then proceeding to look up the words found in that word's definition, etc., also comparing with older dictionaries. Such a process would never end.
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Derrida describes the task of deconstruction as the identification of metaphysics of presence, or logocentrism in western philosophy. Metaphysics of presence is the desire for immediate access to meaning, the privileging of presence over absence. This means that there is an assumed bias in certain binary oppositions where one side is placed in a position over another, such as good over bad, speech over the written word, male over female. Derrida writes, "Without a doubt, Aristotle thinks of
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time on the basis of ousia as parousia, on the basis of the now, the point, etc. And yet an entire reading could be organized that would repeat in Aristotle's text both this limitation and its opposite".[22]:29–67 To Derrida, the central bias of logocentrism was the now being placed as more important than the future or past. This argument is largely based on the earlier work of Heidegger, who, in Being and Time, claimed that the theoretical attitude of pure presence is parasitical upon a more
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originary involvement with the world in concepts such as ready-to-hand and being-with.[citation needed]
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In the deconstruction procedure, one of the main concerns of Derrida is to not collapse into Hegel's dialectic, where these oppositions would be reduced to contradictions in a dialectic that has the purpose of resolving it into a synthesis.[17]:43 The presence of Hegelian dialectics was enormous in the intellectual life of France during the second half of the 20th century, with the influence of Kojève and Hyppolite, but also with the impact of dialectics based on contradiction developed by
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Marxists, and including the existentialism of Sartre, etc. This explains Derrida's concern to always distinguish his procedure from Hegel's,[17]:43 since Hegelianism believes binary oppositions would produce a synthesis, while Derrida saw binary oppositions as incapable of collapsing into a synthesis free from the original contradiction.
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There have been problems defining deconstruction. Derrida claimed that all of his essays were attempts to define what deconstruction is,[26]:4 and that deconstruction is necessarily complicated and difficult to explain since it actively criticises the very language needed to explain it.
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Derrida has been more forthcoming with negative (apophatic) than with positive descriptions of deconstruction. When asked by Toshihiko Izutsu some preliminary considerations on how to translate "deconstruction" in Japanese, in order to at least prevent using a Japanese term contrary to deconstruction's actual meaning, Derrida began his response by saying that such a question amounts to "what deconstruction is not, or rather ought not to be".[26]:1
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Derrida states that deconstruction is not an analysis, a critique, or a method[26]:3 in the traditional sense that philosophy understands these terms. In these negative descriptions of deconstruction, Derrida is seeking to "multiply the cautionary indicators and put aside all the traditional philosophical concepts".[26]:3 This does not mean that deconstruction has absolutely nothing in common with an analysis, a critique, or a method, because while Derrida distances deconstruction from these
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terms, he reaffirms "the necessity of returning to them, at least under erasure".[26]:3 Derrida's necessity of returning to a term under erasure means that even though these terms are problematic we must use them until they can be effectively reformulated or replaced. The relevance of the tradition of negative theology to Derrida's preference for negative descriptions of deconstruction is the notion that a positive description of deconstruction would over-determine the idea of deconstruction
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and would close off the openness that Derrida wishes to preserve for deconstruction. If Derrida were to positively define deconstruction—as, for example, a critique—then this would make the concept of critique immune to itself being deconstructed. Some new philosophy beyond deconstruction would then be required in order to encompass the notion of critique.
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Derrida states that "Deconstruction is not a method, and cannot be transformed into one".[26]:3 This is because deconstruction is not a mechanical operation. Derrida warns against considering deconstruction as a mechanical operation, when he states that "It is true that in certain circles (university or cultural, especially in the United States) the technical and methodological "metaphor" that seems necessarily attached to the very word 'deconstruction' has been able to seduce or lead
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astray".[26]:3 Commentator Richard Beardsworth explains that
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Derrida is careful to avoid this term [method] because it carries connotations of a procedural form of judgement. A thinker with a method has already decided how to proceed, is unable to give him or herself up to the matter of thought in hand, is a functionary of the criteria which structure his or her conceptual gestures. For Derrida [...] this is irresponsibility itself. Thus, to talk of a method in relation to deconstruction, especially regarding its ethico-political implications, would
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appear to go directly against the current of Derrida's philosophical adventure.[27]
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Beardsworth here explains that it would be irresponsible to undertake a deconstruction with a complete set of rules that need only be applied as a method to the object of deconstruction, because this understanding would reduce deconstruction to a thesis of the reader that the text is then made to fit. This would be an irresponsible act of reading, because it becomes a prejudicial procedure that only finds what it sets out to find.
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Derrida states that deconstruction is not a critique in the Kantian sense.[26]:3 This is because Kant defines the term critique as the opposite of dogmatism. For Derrida, it is not possible to escape the dogmatic baggage of the language we use in order to perform a pure critique in the Kantian sense. Language is dogmatic because it is inescapably metaphysical. Derrida argues that language is inescapably metaphysical because it is made up of signifiers that only refer to that which transcends
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them—the signified.[citation needed] In addition, Derrida asks rhetorically "Is not the idea of knowledge and of the acquisition of knowledge in itself metaphysical?"[2]:5 By this, Derrida means that all claims to know something necessarily involve an assertion of the metaphysical type that something is the case somewhere. For Derrida the concept of neutrality is suspect and dogmatism is therefore involved in everything to a certain degree. Deconstruction can challenge a particular dogmatism
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and hence desediment dogmatism in general, but it cannot escape all dogmatism all at once.
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Derrida states that deconstruction is not an analysis in the traditional sense.[26]:3 This is because the possibility of analysis is predicated on the possibility of breaking up the text being analysed into elemental component parts. Derrida argues that there are no self-sufficient units of meaning in a text, because individual words or sentences in a text can only be properly understood in terms of how they fit into the larger structure of the text and language itself. For more on Derrida's
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theory of meaning see the article on différance.
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Derrida states that his use of the word deconstruction first took place in a context in which "structuralism was dominant" and deconstruction's meaning is within this context. Derrida states that deconstruction is an "antistructuralist gesture" because "[s]tructures were to be undone, decomposed, desedimented". At the same time, deconstruction is also a "structuralist gesture" because it is concerned with the structure of texts. So, deconstruction involves "a certain attention to
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structures"[26]:2 and tries to "understand how an 'ensemble' was constituted".[26]:3 As both a structuralist and an antistructuralist gesture, deconstruction is tied up with what Derrida calls the "structural problematic".[26]:2 The structural problematic for Derrida is the tension between genesis, that which is "in the essential mode of creation or movement", and structure: "systems, or complexes, or static configurations".[16]:194 An example of genesis would be the sensory ideas from which
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knowledge is then derived in the empirical epistemology. An example of structure would be a binary opposition such as good and evil where the meaning of each element is established, at least partly, through its relationship to the other element.
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It is for this reason that Derrida distances his use of the term deconstruction from post-structuralism, a term that would suggest that philosophy could simply go beyond structuralism. Derrida states that "the motif of deconstruction has been associated with 'post-structuralism'", but that this term was "a word unknown in France until its 'return' from the United States".[26]:3 In his deconstruction of Husserl, Derrida actually argues for the contamination of pure origins by the structures of
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language and temporality. Manfred Frank has even referred to Derrida's work as "Neostructuralism".[28][page needed]
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The popularity of the term deconstruction, combined with the technical difficulty of Derrida's primary material on deconstruction and his reluctance to elaborate his understanding of the term, has meant that many secondary sources have attempted to give a more straightforward explanation than Derrida himself ever attempted. Secondary definitions are therefore an interpretation of deconstruction by the person offering them rather than a summary of Derrida's actual position.
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* Paul de Man was a member of the Yale School and a prominent practitioner of deconstruction as he understood it. His definition of deconstruction is that, "[i]t's possible, within text, to frame a question or undo assertions made in the text, by means of elements which are in the text, which frequently would be precisely structures that play off the rhetorical against grammatical elements."[29]
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* Richard Rorty was a prominent interpreter of Derrida's philosophy. His definition of deconstruction is that, "the term 'deconstruction' refers in the first instance to the way in which the 'accidental' features of a text can be seen as betraying, subverting, its purportedly 'essential' message."[30][]
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* John D. Caputo attempts to explain deconstruction in a nutshell by stating:"Whenever deconstruction finds a nutshell—a secure axiom or a pithy maxim—the very idea is to crack it open and disturb this tranquility. Indeed, that is a good rule of thumb in deconstruction. That is what deconstruction is all about, its very meaning and mission, if it has any. One might even say that cracking nutshells is what deconstruction is. In a nutshell. ...Have we not run up against a paradox and an aporia
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[something contradictory]...the paralysis and impossibility of an aporia is just what impels deconstruction, what rouses it out of bed in the morning..."[31]
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* Niall Lucy points to the impossibility of defining the term at all, stating: "While in a sense it is impossibly difficult to define, the impossibility has less to do with the adoption of a position or the assertion of a choice on deconstruction's part than with the impossibility of every 'is' as such. Deconstruction begins, as it were, from a refusal of the authority or determining power of every 'is', or simply from a refusal of authority in general. While such refusal may indeed count as a
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position, it is not the case that deconstruction holds this as a sort of 'preference' ".[32][page needed]
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* David B. Allison is an early translator of Derrida and states, in the introduction to his translation of Speech and Phenomena: [Deconstruction] signifies a project of critical thought whose task is to locate and 'take apart' those concepts which serve as the axioms or rules for a period of thought, those concepts which command the unfolding of an entire epoch of metaphysics. 'Deconstruction' is somewhat less negative than the Heideggerian or Nietzschean terms 'destruction' or 'reversal'; it
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suggests that certain foundational concepts of metaphysics will never be entirely eliminated...There is no simple 'overcoming' of metaphysics or the language of metaphysics.
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* Paul Ricœur defines deconstruction as a way of uncovering the questions behind the answers of a text or tradition.[33][]
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* Richard Ellmann defines 'deconstruction' as the systematic undoing of understanding.[]
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A survey of the secondary literature reveals a wide range of heterogeneous arguments. Particularly problematic are the attempts to give neat introductions to deconstruction by people trained in literary criticism who sometimes have little or no expertise in the relevant areas of philosophy that Derrida is working in. These secondary works (e.g. Deconstruction for Beginners[34][page needed] and Deconstructions: A User's Guide)[35][page needed] have attempted to explain deconstruction while being
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academically criticized as too far removed from the original texts and Derrida's actual position.[citation needed]
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Derrida's method consisted of demonstrating all the forms and varieties of the originary complexity of semiotics, and their multiple consequences in many fields. His way of achieving this was by conducting thorough, careful, sensitive, and yet transformational readings of philosophical and literary texts, with an ear to what in those texts runs counter to their apparent systematicity (structural unity) or intended sense (authorial genesis). By demonstrating the aporias and ellipses of thought,
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Derrida hoped to show the infinitely subtle ways that this originary complexity, which by definition cannot ever be completely known, works its structuring and destructuring effects.[36]
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Deconstruction denotes the pursuing of the meaning of a text to the point of exposing the supposed contradictions and internal oppositions upon which it is founded—supposedly showing that those foundations are irreducibly complex, unstable, or impossible. It is an approach that may be deployed in philosophy, in literary analysis, and even in the analysis of scientific writings.[37] Deconstruction generally tries to demonstrate that any text is not a discrete whole but contains several
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irreconcilable and contradictory meanings; that any text therefore has more than one interpretation; that the text itself links these interpretations inextricably; that the incompatibility of these interpretations is irreducible; and thus that an interpretative reading cannot go beyond a certain point. Derrida refers to this point as an "aporia" in the text; thus, deconstructive reading is termed "aporetic."[38] He insists that meaning is made possible by the relations of a word to other words
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within the network of structures that language is.[39]
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Derrida initially resisted granting to his approach the overarching name "deconstruction", on the grounds that it was a precise technical term that could not be used to characterize his work generally. Nevertheless, he eventually accepted that the term had come into common use to refer to his textual approach, and Derrida himself increasingly began to use the term in this more general way.
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Derrida's lecture at Johns Hopkins University, "Structure, Sign, and Play in the Human Sciences", often appears in collections as a manifesto against structuralism. Derrida's essay was one of the earliest to propose some theoretical limitations to structuralism, and to attempt to theorize on terms that were clearly no longer structuralist. Structuralism viewed language as a number of signs, composed of a signified (the meaning) and a signifier (the word itself). Derrida proposed that signs
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always referred to other signs, existing only in relation to each other, and there was therefore no ultimate foundation or centre. This is the basis of différance.[40]
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Between the late 1960s and the early 1980s, many thinkers were influenced by deconstruction, including Paul de Man, Geoffrey Hartman, and J. Hillis Miller. This group came to be known as the Yale school and was especially influential in literary criticism. Derrida and Hillis Miller were subsequently affiliated with the University of California, Irvine.[41]
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Miller has described deconstruction this way: "Deconstruction is not a dismantling of the structure of a text, but a demonstration that it has already dismantled itself. Its apparently solid ground is no rock, but thin air."[42]
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Arguing that law and politics cannot be separated, the founders of the "Critical Legal Studies Movement" found it necessary to criticize the absence of the recognition of this inseparability at the level of theory. To demonstrate the indeterminacy of legal doctrine, these scholars often adopt a method, such as structuralism in linguistics, or deconstruction in Continental philosophy, to make explicit the deep structure of categories and tensions at work in legal texts and talk. The aim was to
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deconstruct the tensions and procedures by which they are constructed, expressed, and deployed.
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For example, Duncan Kennedy, in explicit reference to semiotics and deconstruction procedures, maintains that various legal doctrines are constructed around the binary pairs of opposed concepts, each of which has a claim upon intuitive and formal forms of reasoning that must be made explicit in their meaning and relative value, and criticized. Self and other, private and public, subjective and objective, freedom and control are examples of such pairs demonstrating the influence of opposing
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concepts on the development of legal doctrines throughout history.[3]
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Deconstructive readings of history and sources have changed the entire discipline of history. In Deconstructing History, Alun Munslow examines history in what he argues is a postmodern age. He provides an introduction to the debates and issues of postmodernist history. He also surveys the latest research into the relationship between the past, history, and historical practice, as well as articulating his own theoretical challenges.[6]
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Jean-Luc Nancy argues, in his 1982 book The Inoperative Community, for an understanding of community and society that is undeconstructable because it is prior to conceptualisation. Nancy's work is an important development of deconstruction because it takes the challenge of deconstruction seriously and attempts to develop an understanding of political terms that is undeconstructable and therefore suitable for a philosophy after Derrida.
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Simon Critchley, an English philosopher, argues, in his 1992 book The Ethics of Deconstruction,[43] that Derrida's deconstruction is an intrinsically ethical practice. Critchley argues that deconstruction involves an openness to the Other that makes it ethical in the Levinasian understanding of the term.
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Jacques Derrida has had a great influence on contemporary political theory and political philosophy. Derrida's thinking has inspired Slavoj Zizek, Richard Rorty, Ernesto Laclau, Judith Butler and many more contemporary theorists who have developed a deconstructive approach to politics. Because deconstruction examines the internal logic of any given text or discourse it has helped many authors to analyse the contradictions inherent in all schools of thought; and, as such, it has proved
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revolutionary in political analysis, particularly ideology critiques.[44][page needed]
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Richard Beardsworth, developing from Critchley's Ethics of Deconstruction, argues, in his 1996 Derrida and the Political, that deconstruction is an intrinsically political practice. He further argues that the future of deconstruction faces a perhaps undecidable choice between a theological approach and a technological approach, represented first of all by the work of Bernard Stiegler.
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Derrida was involved in a number of high-profile disagreements with prominent philosophers, including Michel Foucault, John Searle, Willard Van Orman Quine, Peter Kreeft, and Jürgen Habermas. Most of the criticism of deconstruction were first articulated by these philosophers and repeated elsewhere.
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In the early 1970s, Searle had a brief exchange with Jacques Derrida regarding speech-act theory. The exchange was characterized by a degree of mutual hostility between the philosophers, each of whom accused the other of having misunderstood his basic points.[25]:29[citation needed] Searle was particularly hostile to Derrida's deconstructionist framework and much later refused to let his response to Derrida be printed along with Derrida's papers in the 1988 collection Limited Inc. Searle did
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not consider Derrida's approach to be legitimate philosophy, or even intelligible writing, and argued that he did not want to legitimize the deconstructionist point of view by paying any attention to it. Consequently, some critics[45] have considered the exchange to be a series of elaborate misunderstandings rather than a debate, while others[46] have seen either Derrida or Searle gaining the upper hand. The level of hostility can be seen from Searle's statement that "It would be a mistake to
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regard Derrida's discussion of Austin as a confrontation between two prominent philosophical traditions", to which Derrida replied that that sentence was "the only sentence of the 'reply' to which I can subscribe".[47] Commentators have frequently interpreted the exchange as a prominent example of a confrontation between analytic and Continental philosophies.
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The debate began in 1972, when, in his paper "Signature Event Context", Derrida analyzed J. L. Austin's theory of the illocutionary act. While sympathetic to Austin's departure from a purely denotational account of language to one that includes "force", Derrida was sceptical of the framework of normativity employed by Austin. Derrida argued that Austin had missed the fact that any speech event is framed by a "structure of absence" (the words that are left unsaid due to contextual constraints)
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and by "iterability" (the constraints on what can be said, imposed by what has been said in the past). Derrida argued that the focus on intentionality in speech-act theory was misguided because intentionality is restricted to that which is already established as a possible intention. He also took issue with the way Austin had excluded the study of fiction, non-serious, or "parasitic" speech, wondering whether this exclusion was because Austin had considered these speech genres as governed by
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different structures of meaning, or hadn't considered them due to a lack of interest. In his brief reply to Derrida, "Reiterating the Differences: A Reply to Derrida", Searle argued that Derrida's critique was unwarranted because it assumed that Austin's theory attempted to give a full account of language and meaning when its aim was much narrower. Searle considered the omission of parasitic discourse forms to be justified by the narrow scope of Austin's inquiry.[48][49] Searle agreed with
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Derrida's proposal that intentionality presupposes iterability, but did not apply the same concept of intentionality used by Derrida, being unable or unwilling to engage with the continental conceptual apparatus.[46] This, in turn, caused Derrida to criticize Searle for not being sufficiently familiar with phenomenological perspectives on intentionality.[50] Searle also argued that Derrida's disagreement with Austin turned on Derrida's having misunderstood Austin's type–token distinction and
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having failed to understand Austin's concept of failure in relation to performativity. Some critics[50] have suggested that Searle, by being so grounded in the analytical tradition that he was unable to engage with Derrida's continental phenomenological tradition, was at fault for the unsuccessful nature of the exchange.
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Derrida, in his response to Searle ("a b c ..." in Limited Inc), ridiculed Searle's positions. Claiming that a clear sender of Searle's message could not be established, Derrida suggested that Searle had formed with Austin a société à responsabilité limitée (a "limited liability company") due to the ways in which the ambiguities of authorship within Searle's reply circumvented the very speech act of his reply. Searle did not reply. Later in 1988, Derrida tried to review his position and his
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critiques of Austin and Searle, reiterating that he found the constant appeal to "normality" in the analytical tradition to be problematic.[25]:133[46][51][52][53][54][55][56]
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In the debate, Derrida praised Austin's work, but argued that Austin is wrong to banish what Austin calls "infelicities" from the "normal" operation of language. One "infelicity", for instance, occurs when it cannot be known whether a given speech act is "sincere" or "merely citational" (and therefore possibly ironic). Derrida argues that every iteration is necessarily "citational", due to the graphematic nature of speech and writing, and that language could not work at all without the
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ever-present and ineradicable possibility of such alternate readings. Derrida takes Searle to task for attempting to get around this issue by grounding final authority in the speaker's inaccessible "intention". Derrida argues that intention cannot possibly govern how an iteration signifies, once it becomes hearable or readable. All speech acts borrow from a language whose significance is determined by historical-linguistic context, and by the alternate possibilities that this context makes
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possible. This significance, Derrida argues, cannot be altered or governed by the whims of intention.
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Derrida argued against the constant appeal to "normality" in the analytical tradition of which Austin and Searle were paradigmatic examples.[25]:133
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In the description of the structure called "normal," "normative," "central," "ideal,"this possibility must be integrated as an essential possibility. The possibility cannot be treated as though it were a simple accident-marginal or parasitic. It cannot be, and hence ought not to be, and this passage from can to ought reflects the entire difficulty. In the analysis of so-called normal cases, one neither can nor ought, in all theoretical rigor, to exclude the possibility of transgression. Not
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even provisionally, or out of allegedly methodological considerations. It would be a poor method, since this possibility of transgression tells us immediately and indispensably about the structure of the act said to be normal as well as about the structure of law in general.
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Derrida argued that it was problematic to establish the relation between "nonfiction or standard discourse" and "fiction," defined as its "parasite, "for part of the most originary essence of the latter is to allow fiction, the simulacrum, parasitism, to take place—and in so doing to "de-essentialize" itself as it were".[25]:133
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He would finally argue that the indispensable question would then become:[25]:133
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what is "nonfiction standard discourse," what must it be and what does this name evoke, once its fictionality or its fictionalization, its transgressive "parasitism," is always possible (and moreover by virtue of the very same words, the same phrases, the same grammar, etc.)?
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This question is all the more indispensable since the rules, and even the statements of the rules governing the relations of "nonfiction standard discourse" and its fictional"parasites," are not things found in nature, but laws, symbolic inventions, or conventions, institutions that, in their very normality as well as in their normativity, entail something of the fictional.
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In 1995, Searle gave a brief reply to Derrida in The Construction of Social Reality. He called Derrida's conclusion "preposterous" and stated that "Derrida, as far as I can tell, does not have an argument. He simply declares that there is nothing outside of texts..."[57] Searle's reference here is not to anything forwarded in the debate, but to a mistranslation of the phrase "il n'y a pas dehors du texte," ("There is no outside-text") which appears in Derrida's Of Grammatology.[13]:158–159
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In The Philosophical Discourse of Modernity, Jürgen Habermas criticized what he considered Derrida's opposition to rational discourse.[58]
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Further, in an essay on religion and religious language, Habermas criticized Derrida's insistence on etymology and philology[58] (see Etymological fallacy).
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The American philosopher Walter A. Davis, in Inwardness and Existence: Subjectivity in/and Hegel, Heidegger, Marx and Freud, argues that both deconstruction and structuralism are prematurely arrested moments of a dialectical movement that issues from Hegelian "unhappy consciousness".[59][page needed]
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Popular criticism of deconstruction intensified following the Sokal affair, which many people took as an indicator of the quality of deconstruction as a whole, despite the absence of Derrida from Sokal's follow-up book Impostures Intellectuelles.[60]
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Chip Morningstar holds a view critical of deconstruction, believing it to be epistemologically challenged. He claims the humanities are subject to isolation and genetic drift due to their unaccountability to the world outside academia. During the Second International Conference on Cyberspace (Santa Cruz, California, 1991), he reportedly heckled deconstructionists off the stage.[61] He subsequently presented his views in the article "How to Deconstruct Almost Anything", where he stated,
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"Contrary to the report given in the 'Hype List' column of issue #1 of Wired ('Po-Mo Gets Tek-No', page 87), we did not shout down the postmodernists. We made fun of them."[62] https://en.wikipedia.org/wiki/Deconstruction
15328876255410252
#infranodus
15329615531030000
#twinword
15329615651850000
@twinword provides #topic_modeling #sentiment_analysis #word_associations info #lemmatization apis https://www.twinword.com/
1532961632507000000
@infranodus provides #topic_modeling and #word_associations info (in process) http://infranodus.com
153296166112300000
@twinword provides #document_similarity analysis
15329617007080000
@datumbox provides #document_similarity #sentiment_analysis #topic_modeling api http://www.datumbox.com/
153296171971400000
in #twinword api #lemmatization works only for english
15329619492090000
@ibm_watson offers #topic_modeling #sentiment_analysis and #entity_extraction https://www.ibm.com/watson/
153296429706800000
@textalizer performs some very basic #topic_modeling
15332959524840000
@dandelion performs #sentiment_analysis #text_classification #entity_extraction #document_similarity via API (SaS) http://dandelion.eu/
153329926661000000
@wordsapi provides #word_associations and #word_definitions via an API, also has an easy-to-use online tryout http://wordsapi.com
153329943672000000
@eventregistry provides #topic_modeling and #concept_graphs for various news and events around the world (also API) http://eventregistry.org/
153329998454700000
#infranodus also provides #concept_graphs
15333000010800000
@rosette #entity_extraction #topic_modeling #name_matching (main project) via API http://rosette.com
153330086122800000
#rosette also provides #lemmatization
153330086122800020
@monkey_learn provides #entity_extraction and #text_classification API https://monkeylearn.com/
15333015612580000
@meaning_cloud #topic_modeling #sentiment_analysis #text_classification #summarization https://www.meaningcloud.com/
15333016769230000
@aylien #topic_modeling #entity_extraction #sentiment_analysis #summarization https://developer.aylien.com/
15333019440240000
@paralleldots #sentiment_analysis #entity_extraction #intent_analysis (also via API) https://www.paralleldots.com/
15333024593050000
@summarize_bot #entity_extraction #summarization #lemmatization #bias_analysis https://www.summarizebot.com/
15333028683230000
#wordart generates #tag_clouds but its applications are more suitable for entertainment purposes https://wordart.com/
15646846286600000
#tagcroud is an old #tag_clouds generator that has some interesting settings https://tagcrowd.com/
15646847347390000
@wordcloud is an @open_source @python project that creates #tag_clouds https://amueller.github.io/word_cloud/
15646850815430000
@infranodus is an #open_source @text_network_analysis tool that visualizes any text as a network and shows the relations of terms to each other
15646853258250000
#infranodus generates text graphs which can be similar to #tag_clouds http://infranodus.com
15646853941780000
#vader is an #open_source tool that can perform #sentiment_analysis https://www.nltk.org/_modules/nltk/sentiment/vader.html
15646854763990000
@rapidminer is a computer application (no cloud version), which helps perform statistical analysis of data and it also has a #text_network_analysis module https://rapidminer.com/
15647618793010000
#Voyant_Tools is a great tool for text analysis. It works online and offers #tag_clouds as well as interesting statistics and #top_keywords detection https://voyant-tools.org
15647624159460000
@lexos is an educational tool that can detect #top_keywords perform #topic_modelling using kmeans clustering and provides a simple #tag_clouds functionality http://lexos.wheatoncollege.edu/
15647629395500000
@overviewdocs is a text analysis and visualization tool that generates #tag_clouds and provides terms extraction based on #tf_idf algorithm https://www.overviewdocs.com
15647665132590000
@aylien api demo can be used for #topic_modelling and has some #tf_idf functionality as well as #sentiment_analysis https://developer.aylien.com/text-api-demo
15647781439510000
@text_network_analysis can be used for #topic_modelling and has similar functionality to #tf_idf
15647782237540000
#infranodus has #tf_idf statistics
15647782314950000
#voyant_tools has similar #tf_idf functionality
15647782601110000
#top_keywords #tf_idf
15647782723280000
@wtfcsv visualizes #tag_clouds and performs basic #topic_modelling for #csv and #excel files
15647802263180000
@infranodus works with #csv and #excel files
15647802438740000
@lexalytics performs #sentiment_analysis and #text_classification as well as #topic_modeling https://www.lexalytics.com
15780605134430000
@infongen performs #sentiment_analysis and provides #actionable_insights https://www.infongen.com/
15780606369620000
@rapidminer #sentiment_analysis #topic_modeling
15780608830140000
@textgain provides #sentiment_analysis and #topic_modeling and #text_classification
15780611313470000
@monkeylearn provides solutions for #sentiment_analysis and #text_classification as well as #topic_modeling
15780625210280000
Main Topical Groups:
N/A
+ ⤓ ?The topics are the nodes (words) that tend to co-occur together in the same context (next to each other).
We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph and are given a distinct color.
We use a combination of clustering and graph community detection algorithm (Blondel et al based on Louvain) to identify the groups of nodes are more densely connected together than with the rest of the network. They are aligned closer to each other on the graph and are given a distinct color.
Most Influential Elements:
N/A
+ ⤓ ↻ ?We use the Jenks elbow cutoff algorithm to select the top prominent nodes that have significantly higher influence than the rest.
Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
Click the Reveal Non-obvious button to remove the most influential words (or the ones you select) from the graph, to see what terms are hiding behind them.
The most influential nodes are either the ones with the highest betweenness centrality — appearing most often on the shortest path between any two randomly chosen nodes (i.e. linking the different distinct communities) — or the ones with the highest degree.
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Influence Distribution | 0 % |
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| Topics | Nodes in Top Topic | Components | Nodes in Top Comp |
|---|---|---|---|
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| Nodes | Av Degree | Density | |
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Undo Select Export: PNG Gexf Bigrams
Action Advice:
N/A
Structural Gap
(ask a research question that would link these two topics):N/A
?A structural gap shows the two distinct communities (clusters of words) in this graph that are important, but not yet connected. That's where the new potential and innovative ideas may reside.
This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.
This measure is based on a combination of the graph's connectivity and community structure, selecting the groups of nodes that would either make the graph more connected if it's too dispersed or that would help maintain diversity if it's too connected.
Latent Topical Brokers
:
N/A
?These are the latent brokers between the topics: the nodes that have an unusually high rate of influence (betweenness centrality) to their freqency — meaning they may appear not as often as the most influential nodes but they are important narrative shifting points.
These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.
These are usually brokers between different clusters / communities of nodes, playing not easily noticed and yet important role in this network, like the "grey cardinals" of sorts.
Emerging Topics
N/A
Top Relations
:
The most prominent relations between the nodes that exist in this graph are shown above. We treat the graph as undirected by default as it allows us to better detect general patterns.
As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
As an option, you can also downloaded directed bigrams above, in case the direction of the relations is important (for any application other than language).
Main Topics
(according to Latent Dirichlet Allocation):loading...
Most Influential Words
(main topics and words according to LDA):loading...
LDA works only for English-language texts at the moment. More support is coming soon, subscribe @noduslabs to be informed.