Text Analyzer Tools: Comparative Analysis
Posted Sunday, May 28, 2023
Text analyzer tools are used to get insights from unstructured text data. The most common uses include sentiment analysis and text classification, however, most text analyzer software can also be used for topic modeling, entity extraction, keyword research, and other applications.
Most text analysis tools make use of natural language processing (NLP) and machine learning (ML) algorithms to extract insights from text data. These algorithms are trained on large data sets (but not always as some algorithmically derived insights are also possible) and can be used to analyze any text data, including scientific research papers, social media posts, customer reviews, and other types of unstructured data.
As a result, such tools can derive valuable insights that help identify primary themes and emotions within any given text as well as extract the concepts and entities that it is talking about. This makes it an effective approach for a wide range of fields: from marketing and politics to literary studies and research.
In this article, we will review the most popular text analyzer tools, including InfraNodus, and compare them to one another.
Types of Text Analyzer Software
There are multiple types of text analyzer tools. Your choice will mainly depend on your programming skills and access to infrastructure.
1. Text analyzer platforms
If you want to start doing text analysis without any programming knowledge, text analyzer platforms can be an obvious choice. Most do not require any coding and provide cloud infrastructure to run your analysis. However, they are usually limited in terms of the types of analysis they can perform and the amount of data they can process. Most such tools offer graphical interfaces, which makes it easier to derive insights.
Some examples include InfraNodus — a visual text analyzer platform (which offers gap insight analysis that reveals blind spots in a discourse's structure), Voyant-Tools (a simple but free text analysis app for smaller texts), and MonkeyLearn (where you can set your own no-code machine-learning pipeline).
2. Cloud-based API services
Cloud-based services will often not have any consumer-facing graphical interface. Instead, they provide infrastructure and require some programming knowledge to make use of their APIs. They often offer backend dashboards, where some insights can be extracted without any coding. Such tools can be used for larger data sets where visualization is not necessary. The results that they provide can also be easily integrated into other applications.
Some examples include Google Cloud Natural Language AI, Amazon Comprehend, and IBM Watson. Smaller companies, such as TextRazor, MeaningCloud, and Aylien, also offer similar services, but especially TextRazor has very high-quality topic modeling results.
3. NLP libraries in Python and Java
NLP libraries are an obvious choice if you want to program everything yourself and have access to infrastructure or are willing to run everything on your own computer. They are often open-source and can be used for a wide range of applications.
The most popular NLP library used today is Spacy, which is a Python library with almost unlimited functionality.
Top 5 Text Analyzer Tools
Below we describe our favorite text analysis tools in more detail.1. InfraNodus: Topic Modeling and Structural Gap Insights
Our favorite tool is InfraNodus and we are not just saying that because we developed it. A big difference between InfraNodus and other tools is that it provides information not only about what you can find inside a text but also — what's missing. This is done by analyzing the text network structure, identifying the clusters of topics, and revealing the gaps between them.
InfraNodus has a built-in AI module that uses GPT-4 to find connections between these clusters and help generate interesting research questions. The visualization module allows one to find the topical patterns inside the text and makes it easy to understand the main themes and topics, even for non-technical users.
The only disadvantage of InfraNodus is that it cannot be used on huge data sets and it doesn't provide text classification out of the box. That is, you can extract the topics from your documents, but you cannot reorganize the documents into clusters based on this information automatically.
InfraNodus offers:
• text analysis
• topic modeling
• sentiment analysis
• AI-powered insights
• tokenization
• text structure analysis
Price:
• free trial available
• subscription starts at €9 / month
2. TextRazor: High-Quality Named Entity Extraction and Taxonomies
TextRazor is a cloud-based API service that offers a wide range of text analysis options. It's our second-favorite tool because the quality of the data that it provides is outstanding.
One of the core features of TextRazor is entity detection and classification, which it can run even on smaller text snippets (which is not always the case with bigger cloud-based APIs). It also works in multiple languages.
The only disadvantage of TextRazor is that it is limited by 200Kb of data (you have to split your files into chunks to process them) and it offers only a very simple customer-facing interface. Most of the more complex operations have to be performed via its API and require programming knowledge.
TextRazor offers:
• named entity extraction
• topic modeling
• keyword extraction
• sentiment analysis
Price:
• free demo available
• subscription starts at €200 / month
3. Spacy: Powerful and Comprehensive Python Library
If you require maximum flexibility and unlimited functionality, then Spacy would be an obvious choice. It is one of the most popular NLP libraries and has tons of applications. It can also work well with other Python-based tools, such as SciKit learn and others, and there is a large online community that can provide support on using it via StackOverflow.
Spacy requires knowledge of Python and programming, and you would need to set up your own infrastructure. However, an easy way to begin is to set up a Jupyter Notebook (or a cloud-based Google Collab), load the Spacy library in there, and start experimenting with it there. If the capacity becomes insufficient, it is always possible to move the script to a bigger server or to set up your own machine for this purpose.
Spacy offers:
• named entity extraction
• topic modeling
• keyword extraction
• sentiment analysis
• pre-trained word vectors
• text classification
Price:
• free
4. NetBase Quid: Marketing Insights
NetBase Quid is an impressive but very expensive platform, which is mainly used for consumer and market intelligence. It makes use of text visualization and analysis to deliver industry-specific insights, so it may be suitable to those who want to bypass all the technicalities and obtain actionable insights about a brand's health, current trends, customer sentiment, etc.
While Netbase Quid is a text analysis software, its results are usually presented in the shape of reports and data visualizations and you will most likely have to work with someone from Quid to find your way around their software.
Quid Offers:
• text classification
• competitive intelligence
• trend analytics
• topic modeling
• named entity extraction
• sentiment analysis
Price:
• not known, but usually such platforms start at about €30000 per year
5. MonkeyLearn: Design Your Own Machine Learning Pipelines
MonkeyLearn is a text analytics platform that allows you to build your own machine-learning pipelines. You can use ready-made recipes (for instance, for sentiment analysis or entity detection) but you can also train an ML algorithm on your own data to make the results more precise for your particular use case.
MonkeyLearn Offers:
• sentiment analysis
• entity extraction
• topic modeling
Price:
• from $299 / month
How Similar Are Those Tools?
Using the graph below, you can see how the various text analyzer tools are related to one another. Click on the filter menu to see which concepts and functionalities connect them together. Select a tool to read more about it.
Try It Yourself
Create an account to try visual text analysis and reveal insights about gaps in a text structure in InfraNodus:
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