LSTM-sentiment-analysis

Using LSTM for sentiment analysis of IMDB movie reviews and using GloVe Embeddings for analogy tasks

Analogy

• Prince – Boy + Girl = Princess
• King – Man + Woman = Queen
• Chinese – China + India = Indian
• Larger – Large + small = Smaller

We used different dimensional glove embeddings to create the analogies and compared the norm of the resultant vector with the actual result of the analogy. To get the closest matching word vector, we used the most_similar function from genism which finds the closest vector in terms of cosine similarity

Sentiment Analysis using LSTM

We are using Keras libraries to build the LSTM network for sentiment classification.

Dataset: IMBD movie reviews (IMDB movie review sentiment classification dataset, n.d.)

We have chosen reviews of maximum length up to 500 words

Train data = 42882 reviews

Vocabulary = 40000 words

Train validation split = 0.8

Test data = 3065 reviews

Padding was done

Input => embedding layer=>LSTM=>sigmoid