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Table Of Contents
Machine Learning for Emotion Analysis in Python
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Before we can start assigning emotions to texts, we have to carry out a range of preprocessing tasks to get to the elements that carry the information we want. In Chapter 1, Foundations, we briefly covered the various components of a generic NLP system, but without looking in detail at how any of these components might be implemented. In this chapter, we will provide sketches and partial implementations of the tools that are most likely to be useful for sentiment mining – where we give a partial implementation or a code fragment for something, the full implementation is available in the code repository.
We will look at the earlier stages of the language processing pipeline in detail. The texts that are most often used for sentiment mining tend to be very informal – tweets, product reviews, and so on. This material is often ungrammatical and contains made-up words, misspellings, and non-text items such as emoticons...