BiLSTM and Multiple Linear Regression based Sentiment Analysis Model using Polarity and Subjectivity of a Text

Abstract

Sentiment analysis has become more and more requested by companies to improve their services. However, the main contribution of this paper is to present the results of the study which consists in proposing a combined model of sentiment analysis that is able to find the binary polarity of the analyzed text. The proposed model is based on a Bidirectional-Long Short-Term Memory recurrent neural network and the TextBolb model which computes both the polarity and the subjectivity of the input text. These two models are combined in a classification model that implements each of the Logistic Regression, k-Nearest Neighbors, Random Forest, Support Vector Machine, K-means and Naive Bayes algorithms. The training and test data come from the Twitter Airlines Sentiment data set. Experimental results show that the proposed system gives better performance metrics (accuracy and F1 score) than those found with the BiLSTM and TextBlob models used separately. The obtained results well serve organizations, companies and brands to get useful information that helps them to understand a customer's opinion of a particular product or service.