A Comparative Study of Deep Learning and Machine Learning Algorithm for Sentiment Analysis

Abstract

Sentiment analysis is a classification procedure where we apply machine learning and deep learning algorithms to analyze the sentiment of the dataset, which consists of text, e.g., a message that can be of positive or negative sentiment. In this study, an attempt has been made to investigate which sentiment analysis techniques are feasible for product reviews. Here, the Amazon reviews dataset is used to compare, train, and test various machine learning and deep learning methods having product reviews from Amazon, which are chosen randomly from an open-source repository. The dataset comprises 4 million reviews. Comparison of several algorithms' performances, i.e., RFC, XGBC, LGBM, MNB, GBC, DTC, and Bi-LSTM, amongst which Bi-LSTM gives the highest performance among the algorithms used for classification. It was also applied to the other reviews from the Amazon dataset to predict the sentiment of the reviews, as well as a fresh Amazon scraped dataset comprising product reviews from several categories. This resulted in a very accurate classification, with the best results for test reviews on the amazon dataset. In conclusion, Bi-LSTM networks are excellent for categorizing customer sentiment on product reviews, and the results do not differ considerably across categories.