Sentiment analysis is a systematic way to identify, extract, quantify, and study effective states and subjective information. This is widely used in natural language processing (NLP), text analytics, and computational linguistics. This chapter demonstrates how to implement and deploy a hands-on deep learning project that classifies review texts as either positive or negative based on the words they contain. A large-scale movie review dataset that contains 50k reviews (training plus testing) will be used.
A combined approach using Word2Vec (that is, a widely used word embedding technique in NLP) and the Long Short-Term Memory (LSTM) network for modeling will be applied: the pre-trained Google news vector model will be used as the neural word embeddings. Then, the training vectors, along with the labels, will be fed into the LSTM...
