Sentiment Analysis with GRU

Sentiment analysis is a popular use case for applying natural language processing techniques. The aim of sentiment analysis is to determine whether a given piece of text can be considered as conveying a 'positive' sentiment or a 'negative' sentiment. For example, consider the following text reviewing a book:

"The book had its moments of glory, but seemed to be missing the point quite frequently. An author of such calibre certainly had more in him than what was delivered through this particular work."

To a human reader, it is perfectly clear that the mentioned book review conveys a negative sentiment. So, how would you go about building a machine learning model for the classification of sentiments? As always, for using a supervised learning approach, a text corpus containing several samples is needed. Each piece of text in this corpus should have a label indicating whether the text can be mapped to a positive or a negative sentiment...