Summary

We started our journey into NLP with basic text analytics and text preprocessing techniques, such as tokenization, stemming, lemmatization, and lowercase conversion, to name a few. We then explored ways in which we can represent our text data in numerical form so that it can be understood by machines in order to implement various algorithms. After getting some practical knowledge of topic modeling, we moved on to text vectorization, and finally, in this chapter, we explored various applications of sentiment analysis. This included different tools that use sentiment analysis, from technologies available from online marketplaces to deep learning frameworks. More importantly, we learned how to load data and train our model to use it to predict sentiment.