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Machine Learning for Emotion Analysis in Python
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Although we have seen that there are many sentiment and emotion datasets available, it is rare that a dataset meets all the exact requirements. However, there are ways to tackle this problem.
We have seen some datasets labeled for sentiment, some for emotion, and also others labeled for more exotic things such as valence that do not seem to fit into what we are looking for: the emotion analysis problem. However, in certain circumstances, it is still possible to repurpose and use these. For example, if a dataset contains emotions, we can transform this into a sentiment dataset simply by assuming that the “anger” emotion is a negative sentiment and the “joy” emotion is a positive sentiment. A degree of subjectivity and manual analysis of the individual dataset is then required to determine which emotions would constitute a good substitute for the neutral sentiment. In our experience, this is typically not straightforward.
Data transformation...