Analysis of hyperparameters in Sentiment Analysis of Movie Reviews using Bi-LSTM

Abstract

Movie reviews are an important factor in determining a film’s success because instead of depending solely on the number of views as a parameter for the success of the movie, movie reviews are used to acquire additional insights into the movie. Existing systems use LSTM for sentiment analysis but there is no study available how various hyperparameters affect the performance of the model. Bi-LSTM along with dropout layers provide good accuracy in sentiment analysis. The suggested method outperforms CNN and Natural Language Toolkit in terms of accuracy.The proposed model is tested using different hyper parameters including dropout rate,number of Bi-LSTM layers and Bi-LSTM nodes. 64 LSTM nodes, 2 Bi-directional Layers, and a 0.2 Dropout rate should be used for optimal accuracy. Effect of different text vectorization algorithms and activation functions was also studied. The combination of Tf-idf text vectorization and the ReLU activation function yields the best results.