As technology is increasing the abilities of businesses, sentiment analysis is becoming a more commonly utilized tool for various use cases. Businesses use sentiment analysis to give their users insights into how the customer feels regarding their business, products, and topics of interest.
Sentiment analysis is basically a method of computationally identifying and categorizing sentiments expressed in a piece of text or corpus in order to determine whether the composer's attitude towards a particular topic, product, and so on is positive, negative, or neutral. Sentiment analysis algorithms use NLP to classify documents as positive, neutral, or negative.
In this recipe, you will learn how to develop deep learning models for sentiment analysis, including:
- How to preprocess and load a dataset in Keras
- How to use word embeddings
- How to develop a large neural network model for sentiment analysis