#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]
@text_mining tasks include #text_categorization, #text_clustering, #concept_extraction, production of #granular_taxonomies, #sentiment_analysis, #document_summarization
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[sentiment_analysis]
@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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[esa]
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]
@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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text categorization
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data mining is a tool for allowing users to
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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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