LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model

Abstract

Most sentiment analysis models that use supervised learning algorithms consume a lot of labeled data in the training phase in order to give satisfactory results. This is usually expensive and leads to high labor costs in real-world applications. This work consists in proposing a hybrid sentiment analysis model based on a Long Short-Term Memory network, a rule-based sentiment analysis lexicon and the Term Frequency-Inverse Document Frequency weighting method. These three (input) models are combined in a binary classification model. In the latter, each of these algorithms has been implemented: Logistic Regression, k-Nearest Neighbors, Random Forest, Support Vector Machine and Naive Bayes. Then, the model has been trained on a limited amount of data from the IMDB dataset. The results of the evaluation on the IMDB data show a significant improvement in the Accuracy and F1 score compared to the best scores recorded by the three input models separately. On the other hand, the proposed model was able to transfer the knowledge gained on the IMDB dataset to better handle a new data from Twitter US Airlines Sentiments dataset.