-
Book Overview & Buying
-
Table Of Contents
Machine Learning for Emotion Analysis in Python
By :
In this chapter, we started by examining emotion and sentiment, and their origins. Emotion is not the same as sentiment; emotion is more fine-grained and is much harder to quantify and work with. Hence, we learned about the three main theories of emotion, with psychologists, neurologists, and cognitive scientists each having slightly different views as to how emotions are formed. We explored the approaches of Ekman and Plutchik, and how the categorical and dimensional models are laid out.
We also examined the reasons why emotion analysis is important but difficult due to the nuances and difficulty of working with content written in natural language, particularly the kind of informal language we are concerned with in this book. We looked at the basic issues in NLP and will return to the most relevant aspects of NLP in Chapter 4, Preprocessing – Stemming, Tagging, and Parsing. Finally, we introduced machine learning and worked through some sample projects.
In the next chapter, we will explore where to find suitable data, the steps needed to make it fit for purpose, and how to construct a dataset suitable for emotion analysis.