Authors : NAVIN R.; NIVESH SB; VIGNESHWARAN M.

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/v34yf6we

Scribd : https://tinyurl.com/2zdfmajb

DOI : https://doi.org/10.5281/zenodo.14557727

Abstract : In today’s online shopping world, product reviews significantly impact customer purchasing decisions, but the vast number of reviews makes it difficult for businesses to analyze them manually. This project uses Natural Language Processing (NLP) to automate sentiment analysis, allowing businesses to quickly understand customer opinions. By categorizing reviews as positive, negative, or neutral, the project provides valuable insights into customer sentiment. The process begins by gathering and cleaning a dataset of product reviews, followed by steps like removing unnecessary words, breaking down sentences, and simplifying words for more accurate analysis. With these preparations, machine learning models such as Naive Bayes and Support Vector Machines (SVM) predict sentiment trends in new reviews, which are then visualized in pie charts for clarity. This automation helps businesses grasp customer needs, leading to improvements in marketing, product development, and customer service. Ultimately, this system allows companies to turn vast amounts of feedback into actionable insights, making it easier to create customer-centered products and strategies.

References :

1. M. Sharma, "Sentiment Analysis of Amazon Reviews Using Natural Language Processing," *International Journal of Data Science*, vol. 12, no. 4, pp. 123-135, 2023.
2. A. Gupta & P. S. R. Kumar, "Leveraging TextBlob for Sentiment Analysis in E-Commerce," *Journal of E-Commerce and Digital Marketing*, vol. 15, no. 2, pp. 55-70, 2022. 
3. R. Patel, "An Overview of Sentiment Analysis and Its Application to Customer Reviews," *Journal of Business Intelligence*, vol. 10, no. 1, pp. 98-110, 2021. 
4. K. L. Johnson, "Scraping and Analyzing Product Reviews: A Web-Based Approach," *Web Analytics and Applications Journal*, vol. 8, no. 3, pp. 210-225, 2020. 
5. A. Williams & H. Zhang, "Text Mining and Sentiment Analysis for E-Commerce Reviews," *International Journal of Data Analytics*, vol. 14, no. 5, pp. 145-160, 2022.
6. J. L. Morgan, "The Use of NLP for Customer Feedback Analysis in Retail," *Journal of Retail Technology*, vol. 9, no. 4, pp. 145-158, 2021. 
7. T. G. Smith, "Trends in E-Commerce Sentiment Analysis: An Overview of Tools and Techniques," *E-Commerce Data Science Review*, vol. 17, no. 2, pp. 79-92, 2023. 
8. B. M. Davis, "A Comparative Study of TextBlob and Vader for Sentiment Analysis," *Journal of Natural Language Processing*, vol. 20, no. 3, pp. 88-103, 2020. 
9. P. Kumar & N. Singh, "Deep Learning Techniques in Sentiment Analysis for Product Reviews," *Advances in Artificial Intelligence and Machine Learning*, vol. 18, no. 1, pp. 36-49, 2021.