Machine Learning-based Multiclass Classification Methods for Sentiment Analysis

Sentiment analysis, also known as opinion mining, is the process of identifying the sentiment or emotion conveyed in a textual review. This requires categorizing the expressed opinions into several sentiment classes, namely positive, negative, or neutral. Typically, machine learning algorithms are employed to construct a sentiment classifier, which is subsequently utilized to automatically assign appropriate sentiment to individual textual reviews. Numerous machine learning methods have been utilized for these purposes. Determining the most suitable algorithm for sentiment analysis is a challenge. One potential methodology is doing a comparative examination of the algorithm’s performance with the dataset under consideration, and then choose the most optimal sentiment classifier for adoption. In this study, we conducted a comparative analysis of many machine learning algorithms with lexicon-based approach, including multinomial naïve bayes, support vector machine, k-nearest neighbors, random forest and an ensemble approach combining these algorithms, with the purpose of developing a sentiment classifier model using the TripAdvisor dataset. The objective was to classify hotel customer reviews into three distinct categories: positive, neutral, and negative. After evaluation of recall, precision, F1, and accuracy metrics, it can be concluded that the ensemble approach yields superior outcomes compared to other approaches.