An Exploring the Power of Feature Representations: An Empirical Study on Product Reviews for Sentiment Analysis

With the rise of e-commerce and online shopping, customer reviews have become a crucial factor in determining the quality and reputation of a product. Online shoppers rely heavily on customer reviews to make informed purchasing decisions, as they don't have the opportunity to physically examine the product before buying. As a result, companies are also investing in sentiment analysis to understand and respond to customer feedback, as well as to enhance the quality of their products and services. Using natural language processing (NLP) and machine learning techniques, sentiment analysis classifies the tone of a customer review as positive, negative, or neutral. It involves analysing text data to determine the overall tone, emotion, and opinion expressed in a review. In this work, we study sentiment analysis of client reviews using machine learning algorithms with different vectorization techniques. The strategy outlined here consists of three distinct phases. The initial step involves some pre-processing to get rid of irrelevant information and find the useful terms. Then, feature extraction was accomplished utilizing numerous vectorization strategies as Bag-Of-Words (BoW), Term Frequency Inverse Document Frequency (TF-IDF), and N-grams. After extracting the features from text data, the final stage is classification and predictions based on machine learning approaches. We evaluated the proposed models on Yelp reviews dataset. The experimental results are evaluated using metrics such as precision, recall, and f1-score, and K-fold cross-validation.