Advancements in Machine Learning for Robust Sentiment Analysis of Consumer Product Reviews

Abstract

In this age of social media and huge data, sentiment analysis has become an essential activity. In order for businesses to measure consumer happiness and make educated decisions, it is crucial to understand the feelings conveyed in product reviews. The purpose of this study is to simulate and evaluate an enhanced machine learning approach to product review sentiment analysis. The goal is to create a powerful model for sentiment analysis that can beat current methods in terms of efficiency and accuracy. In this paper, we present a new approach to sentiment analysis in product reviews by integrating state-of-the-art feature extraction with sentiment classification algorithms and model optimization techniques. We begin by outlining the significance of sentiment analysis and the difficulties encountered by current approaches. Additionally, it specifies the aims and parameters of this study. The section on similar studies provides a thorough analysis of the literature and draws attention to the shortcomings of previous methods. In the methodology part, we lay out the specifics of our improved machine learning strategy and the thinking behind the methods we chose. In the results analysis, we test how well our model does on a variety of product review datasets. We compare our results to those of baseline models and cutting-edge sentiment analysis systems, and we provide the accuracy, precision, recall, and F1-score measures. Our discussion also covers the model's ability to handle different kinds of items and reviews.

In comparison to more conventional approaches, our study shows that sentiment analysis is far more accurate. To demonstrate the model's efficacy in various contexts and to highlight its flaws, we use tables and graphs. At the end of the study, we go over some of the possible business uses, suggestions for further studies, and consequences of our results. In sum, this study aids in the development of sentiment analysis methods and gives a great resource for companies who want to learn more about how customers feel about their products through reviews..