The Best Textual Analysis Tools for Data Scientists

Textual analysis is a powerful way for data scientists to extract insights from data. With the right textual analysis tools, data scientists can quickly and accurately analyze large amounts of text data. In this blog post, we’ll take a look at some of the best textual analysis tools available to data scientists today.

Lexalytics

Lexalytics is a leading textual analysis platform that specializes in sentiment analysis, topic extraction, and keyword extraction. It supports multiple languages, and its API is straightforward to use, making it a great option for data scientists who want to quickly and accurately analyze text data. Lexalytics also offers a variety of solutions for analyzing text data, including its enterprise-level Salience Engine.

IBM Watson

IBM Watson is a powerful textual analysis tool that offers a range of features for extracting insights from text data. With Watson, data scientists can quickly and accurately analyze large amounts of text data and extract insights such as sentiment analysis and keyword extraction. Watson also offers a range of visualizations for data exploration, making it a great choice for data scientists looking for a comprehensive solution for textual analysis.

Google Cloud Natural Language API

Google Cloud Natural Language API is a powerful tool for analyzing text data. It offers a range of features for extracting insights from text data, including sentiment analysis, entity extraction, and syntax analysis. The API is straightforward to use, and its visualizations are great for exploring data.

Stanford CoreNLP

Stanford CoreNLP is a powerful open-source text analysis tool for data scientists. It offers a wide range of features for extracting insights from text data, including sentiment analysis, entity extraction, and syntactic analysis. It is also highly customizable, making it a great choice for data scientists who want to tailor their textual analysis.

Conclusion

Textual analysis is a powerful way for data scientists to extract insights from text data. With the right textual analysis tools, data scientists can quickly and accurately analyze large amounts of text data and extract insights such as sentiment analysis, keyword extraction, and entity extraction. The tools listed in this blog post are some of the best textual analysis tools available to data scientists today.

We hope this article has been helpful in helping you find the right textual analysis tool for your data science projects. If you need help getting started with textual analysis, this guide from Towards Data Science is a great resource. Additionally, MonkeyLearn and AYLIEN both offer powerful tools for textual analysis that you may want to explore.