Summary
In this chapter, we discussed two important text analysis problems – sentiment analysis and building a chatbot. Sentiment analysis refers to the task of understanding sentiment in the text, and we have seen the various libraries, algorithms, and approaches to perform this task. A crucial part of performing such tasks is gathering data – we then saw how to download data from internet sources such as Twitter or Reddit. The final section of the chapter focused on how to build chatbots. We explored it from both a historical and theoretical point of view and explored Python libraries that help us easily build chatbots. This brings us to the end of the book – you would now be confident in analyzing text the way you see fit, with a variety of techniques, approaches, and settings. We focused on using the most efficient Python open source libraries, with a focus on Gensim, spaCy, Keras, and scikit-learn throughout the book, while still discussing the other Python text analysis libraries available...