Summary
We have reached the end of this relatively long chapter. Here, we have described three NLP case studies, each one solved by training an LSTM-based RNN applied to a time series prediction kind of problem.
The first case study analyzed movie review texts to extract the sentiment hidden in it. We dealt there with a simplified problem, considering a binary classification (positive versus negative) rather than considering too many nuances of possible user sentiment.
The second case study was language modeling. Training an RNN on a given text corpus with a given style produced a network capable of generating free text in that given style. Depending on the text corpus on which the network is trained, it can produce fairy tales, Shakespearean dialogue, or even rap songs. We showed an example that generates text in fairy tale style. The same workflows can be easily extended with more success to generate rap songs (R. Silipo, AI generated rap songs, CustomerThink, 2019, https:...
