Text analyzer tools are used to get insights from unstructured text data. They can reveal recurrent themes, sentiment, and main entities present within a wide range of documents, as well as categorize them into different clusters. This can be very useful in a wide range of fields: from marketing and sociology to politics and literary studies.

For example, we can analyze hundreds of survey responses to discern what the participants are discussing in a negative context. This can help us understand what issues should be addressed to improve a particular situation. Alternatively, we could categorize responses into clusters in order to understand the different types of customers we are dealing with.

Most text analysis tools are based on a combination of machine learning (ML) and natural language processing (NLP) algorithms. They offer sentiment analysis, text categorization, entity detection, keyword extraction, and topic modeling.

The choice of the best tools depends on your programming knowledge and access to infrastructure. Generally, there are 3 types of text analysis software tools available today:

1) Text analysis platforms (vertically integrated, perfect for end-users, no coding required);
2) AI-powered analysis tools (good for research and personal use, require text prompting);
3) Cloud-based API services (offer a wide range of text analysis and infrastructure, require the knowledge of coding);
4) NLP and ML libraries (require programming knowledge and infrastructure, but offer the highest flexibility).

In the graph on the right, made using InfraNodus, we gathered all the most popular and useful tools, as of 2025, that can be used for text analysis. Using the graph, you can see how the relate to another another: which ones are similar or different. Here are our favorite ones:

1. InfraNodus: Visual AI Text Analysis

InfraNodus is a visual text analysis platform based on NLP and ML approaches. It offers topic modeling, sentiment analysis, keyword and entity extraction, but is less useful for text categorization.

2. Voyant Tools: Comparative Text Analysis

A popular free text analysis tool available online. It provides some basic statistics on texts and trends, so it can be used used for topic modeling and keyword extraction. However, it doesn’t really work for bigger texts.

3. ChatGPT: Chat with Your PDFs

ChatGPT works best if you upload several documents in a project and ask the AI to provide a summary and retrieve the main topics. You can also upload CSV and ChatGPT can launch a Python instance to analyze the data in your spreadsheet making it a powerful tool for analyzing customer data. You might have concerns about the data security though and to get quality insights you need to know how to prompt it well.

4. Google Natural Language Cloud: Powerful APIs

Has a wide range of API-based tools for topic modeling and keyword extraction. Doesn’t cost too much but requires some programming knowledge (although if you understand how their dashboard works, you can use it with no code).

5. NotebookLM: Great for Students

Best known for its ability to create podcasts from documents, NotebookLM is also a text analysis powerhouse. You can ask it to generate an AI summary, timeline, talking points, and even a mind map. Great for students and research where you don’t need to go too much in-depth with analysis but just need to get a general idea about a collection of documents.