Abstract: This study delves into the realm of sentiment analysis techniques with a focus on unraveling customer opinions and reviews concerning products. In an era where online shopping and digital engagement have become ubiquitous, understanding customer sentiment is paramount for businesses to thrive. The project employs a Random Forest Classifier model integrated into a web application for real-time sentiment analysis. Through preprocessing text data and utilizing natural language processing tools, the model discerns between positive and negative sentiments expressed in customer reviews. The findings of this investigation shed light on the efficacy of sentiment analysis techniques in deciphering product sentiment, thereby aiding businesses in making informed decisions to enhance customer satisfaction and product quality.
Keywords: Sentiment Analysis, Customer Reviews, Product Sentiment, Natural Language Processing (NLP), Machine Learning, Random Forest Classifier, Text Preprocessing, Real-time Analysis.
Cite:
Mrs. N. Malathi, T. Dhana lakshmi, M. Praisy Rivritha, K. Pujitha Krishna Priya, M. Tulasi, G. Mani Deepthi, "UNMASKING PRODUCT SENTIMENT: AN INVESTIGATION INTO SENTIMENT ANALYSIS TECHNIQUES FOR UNVEILING CUSTOMER OPINIONS AND REVIEWS", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13314.
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DOI:
10.17148/IJARCCE.2024.13314
