Detecting Customer Emotions to Make Predictions

Sentiment analysis relies on the principle of compositionality. If we cannot understand parts of a sentence, how can we understand a whole sentence? Is this tough task possible for NLP transformer models? We will try several transformer models in this chapter to find out.

We will start with the Stanford Sentiment Treebank (SST). The SST provides datasets with complex sentences to analyze. It is easy to analyze sentences such as "The movie was great." What happens if the task becomes very tough with complex sentences such as "Although the movie was a bit too long, I really enjoyed it."? This sentence is segmented. It forces a transformer model to understand not only the structure of the sequence but also its logical form.

We will then test several transformer models with complex sentences and some simple sentences. We will find that no matter which model we try, it will not work if it wasn't trained enough...