Sentiment analysis of amazon reviews using machine learning classifier

Abstract

Reviews can significantly impact a company's reputation in the market, potentially influencing its overall business outcomes, either positively or negatively. This is especially crucial for companies that operate primarily through e-commerce platforms. Hence, it is vital for companies to pay close attention to customer reviews. Sentiment Analysis, often referred to as "opinion mining,"is a significant procedure in Natural Language Processing (NLP) which serves the purpose of ascertaining the emotional tone of a provided text and categorizing it into positive, negative, or neutral perspectives. In this paper, sentiment analysis methodology is presented for classifying Amazon reviews which utilizes a large dataset of reviews and employs Multinomial Naïve Bayesian (MNB), Support Vector Machine (SVM), Maximum Entropy (ME), and Logistic Regression as the primary classifiers by the authors. With the aid of machine learning, we employed a supervised learning approach to an extensive Amazon dataset in order to categorize it based on sentiment polarity, achieving a high level of accuracy for the results. Here, we utilized the Kaggle dataset that includes a substantial volume of reviews and associated metadata which comprises customer reviews and ratings on Amazon products.