The Best Text Analysis Tools in 2025
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Text analysis tools are used to get insights from unstructured text data. The most common uses include sentiment analysis and text classification, however, text analysis software can also be used for topic modeling, entity extraction, word frequency analysis, 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.
Top 8 Text Mining and Analysis Tools: NLP and AI Software
Below we present our favorite text mining and analysis tools as of 2025. We review this list every year to make sure that we feature the best and most useful software. We make sure to include both traditional NLP tools and the newer AI alternatives, making this list useful for both professional and personal applications.1. InfraNodus: Topic Modeling and Content Gap Analysis, AI Summarization
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 the newest GPT-4o models 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 (> 3 Mb), however, this limitation can be circumvented by dividing your data into categories or sectors and analyzing them separately. Its advanced capabilities also require the users to be proficient with the interface, which might take a moment to learn.
Pros:
• Visualizes the main topics and concepts as an interactive knowledge graph
• Uses network science for advanced insights
• Content gap detection
• Affordable starter plans
• Built-in AI text analysis
Cons:
• 3 Mb context window
• Advanced use cases have a learning curve
• Offers limited text classification
InfraNodus features:
• text analysis
• topic modeling
• sentiment analysis
• AI-powered insights
• tokenization
• text structure analysis
• API access
Price:
• free trial available
• subscription starts at €19 / month
2. ChatGPT: AI Text Analysis
ChatGPT is a champion among AI tools when it comes to text analysis. While it may have a smaller context window than Google Gemini, its ability to organize work in projects, upload several documents to a specific context, run data analysis code for advanced insights, and search capabilities make it the best option among all the AI incumbents.
ChatGPT is especially suitable for uploading a batch of PDF documents and asking the AI to summarize the content or retrieve the main topics from them. Its ability to chat with the documents can be very useful for extracting insights from them. Another powerful feature is the ability to run Python code automatically to extract insights from spreadsheets and Google sheets / CSV documents. Its search and deep research features can be used to identify market trends.
Pros:
• Advanced AI capabilities
• Search and deep research features
• Basic data science workflows
Cons:
• Limited context window (up to 20 documents or 1 Mb)
• Absence of NLP tools and approaches
• Text categorization not possible
• Requires prompting knowledge
ChatGPT features:
• document analysis
• entity extraction
• sentiment analysis
• AI-powered insights
• API access
Price:
• free tiers available
• subscription starts at $24 / month
3. TextRazor: High-Quality Named Entity Extraction, NLP, and Taxonomies API
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.
Pros:
• High-quality insights
• Affordable price
• Powerful API
Cons:
• 200kb batch limit
• Absence of front-end
• Lacks standard AI capabilities
TextRazor features:
• named entity extraction
• topic modeling
• keyword extraction
• sentiment analysis
Price:
• free demo available
• subscription starts at €200 / month (free option available)
4. Spacy: Powerful and Comprehensive Python NLP 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.
Pros:
• Powerful and customizeable
• Free to set up
• Can be combined with other tools
• Self-hosted and open-source
Cons:
• Programming skills required
• Takes long to set up and learn
• Requires a server
Spacy Offers:
• named entity extraction
• topic modeling
• keyword extraction
• sentiment analysis
• pre-trained word vectors
• text classification
Price:
• free
5. NotebookLM: AI-Driven Text Analysis
NotebookLM is an amazing tool from Google that is best known for its ability to create podcasts. However, it can be very useful for text analysis and is in fact used by many researchers to extract insights from scientific publications and books.
NotebookLM can ingest multiple documents and then use the AI to generate a summary, a study guide, a timeline of topics, and even a mind map of the main concepts inside the text. The best way to use it is to give it custom prompts, such as "extract the main topics" or "find the gaps in this text" to generate custom insights that go beyond generalities.
Pros:
• Free version available
• High-quality AI-generated summaries
• Can generate podcasts, mind maps, and study guides
Cons:
• Limited analysis capabilities
• May be not suitable for customer voice analysis
• May seem simplistic
• No API
Google NotebookLM Offers:
• sentiment analysis
• concept extraction
• timeline analysis
Price:
• free tier available
6. MaxQDA: Text Analytics Platform
MaxQDA is a desktop-based text analytics platform. Its main advantage is its ability to code the texts they're analyzing and to then visualize the frequency of those codes across different documents.
MaxQDA can be used for thematic analysis, interview transcription, and literature reviews. The basic workflow is to upload a collection of documents, code them manually (or using the AI paid add on), and then analyze how frequently those coded segments appear. For instance, if you're analyzing interviews, you can code positive / negative sentiment and topics, and see which of those topics come up with the negative connotation.
The problem is that you have to do quite a lot of manual coding work before being able to get insights, which requires learning the product. However, once you get the knack of it, MaxQDA can be a very powerful tool.
Pros:
• Powerful coding and analysis features
• Great for extracting common themes from multiple documents
• A mature product with many features and tutorials
• Has a free demo
Cons:
• Limited analysis for single documents (only tag cloud and timeline)
• AI capabilities are limited to coding and summarization
• Requires manual coding
• Steep learning curve
• Has a desktop app only
• No API
MAXQDA Offers:
• multiple text analysis functionalities
• powerful coding and analysis features
• timeline visualization
• code frequency visualization
Price:
• from $500 / year
7. Lexalytics: Text Analysis Platform
Lexalytics is an impressive but expensive platform, which is mainly used for analyzing open-ended surveys, customer feedback, large text datasets, and social media monitoring. It makes use of text visualization and analysis to deliver insights and also offers an API.
Lexalytics is best suited for industry-specific use cases and in order to get an account, your demands need to be quite high. It is pretty difficult to set up a demo account and their pricing is very opaque, which means that they probably work more as a service agency rather than a standard text analysis tool in the traditional SaaS sense.
Pros:
• Good for industry-specific use cases
• Can process large datasets
• Can be used for trend monitoring
Cons:
• High entry barrier
• Expensive
• Does not have a full demo
Lexalytics Offers:
• text classification
• competitive intelligence
• trend analytics
• topic modeling
• named entity extraction
• sentiment analysis
Price:
• not known, but usually such platforms start at about €5000 per year
8. Voyant Tools: Browser-Based Text Analysis
Voyant Tools is a browser-based text analysis tool that can be used to extract general insights from text: the most freqently used terms and their evolution over time.
One of the special features of this tool is that when you upload several documents at once, you can compare the frequency of different keywords in text to see how those texts are similar or different to each other. However, in order to use this feature you need to take some time to learn how it works and also it is not available via an API. There are also no AI features.
Pros:
• Free for life
• Has unique timeline analysis features
• Offers a web browser version
Cons:
• Limited functionality
• Steep learning curve
• No AI text analysis features
• No API
Voyant Tools Offers:
• sentiment analysis
• entity extraction
• topic modeling
Price:
• from $299 / month
Types of Text Analysis 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. AI Text Analysis Tools
Major AI tools, such as ChatGPT, Gemini and Claude can be used for text analysis, especially for writing, research, and basic customer review or product feedback analysis. The only problem is that as you user, you need to know what you should be asking, so it makes sense to learn some terminology first and to then create custom prompts to extract the insights you need.
The AI tools that can run Python code under the hood are preferable in this case, because when you upload a spreadsheet or a CSV file, you can extract structured insights: for instance, analyzing the customer reviews that have positive sentiment or extracting the main topics from the rows that have a certain value range. These are, however, harder to use when you want to integrate them into your own workflows as you then need to have programming skills to ensure that their output is structured and stable and can be integrated into your other software via API.
3. 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.
4. 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.
If you are interested in text analysis, you might also be interested to learn about the best text visualization tools that can be used to find patterns and visualize insights in text data.
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