Building a Sentiment Classification model to label the sentiment by positive or negative.
The objective of this project is to build a text classification model which analyses the customer's sentiments based on their reviews in the IMDB database. In this project, we have used IMDb dataset to learn word embeddings as we train our dataset. This dataset contains 25,000 movie reviews from IMDB, labeled with a sentiment (positive or negative. We have generated the word embeddings and retrieve the output of each layer of the model.