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) Cloud-based API services (offer a wide range of text analysis and infrastructure, require the knowledge of coding);
3) 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 2023, 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 some highlights:
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.
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.
Using this platform, you can apply ready-made ML recipes to your text analysis workflow (e.g. sentiment analysis) or create your own workflows based on your training data. Suitable for basic and advanced analysis, but quite pricey once you have to process more data.
Google Natural Language Cloud
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).
A text mining platform that offers a wide range of text analysis insights: from sentiment analysis to entity extraction.