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Volume - 6 | Issue - 3 | september 2024

Sentiment Analysis of Product Reviews using Rule-based and Deep-Learning Models Open Access
K. Surendra.  , K. Nithin Prakash., J. Maruthi Kumar., G. Rakesh Goud., N. Shanmugapriya  189
Pages: 301-311
Cite this article
Surendra., K., K. Nithin Prakash., J. Maruthi Kumar., G. Rakesh Goud., and N. Shanmugapriya. "Sentiment Analysis of Product Reviews using Rule-based and Deep-Learning Models." Journal of Trends in Computer Science and Smart Technology 6, no. 3 (2024): 301-311
Published
15 October, 2024
Abstract

This study analyses the feelings and opinions expressed in product reviews by utilizing NLTK (Natural Language Toolkit) and deep learning models to forecast customer sentiments and evaluate the probability of product purchases based on the review information available. The study examines the sentiment analysis applied to the Flipkart product reviews using a sentiment dataset. The NLTK, VADER, and RoBERTa models are evaluated for their effectiveness in predicting the sentiment of the customers. The analysis shows that the VADER, as a rule-based model is efficient in handling short and simple reviews but faces challenges with more complex sentiments. Meanwhile, RoBERTa outperforms VADER, with a Mean Absolute Error (MAE) of 0.12 and an R2 value of 0.85. The comparative study shows the ability of RoBERTa to capture subtle emotions in customer reviews and accurately understand customer feedback, proving to be valuable in e-commerce for optimizing product recommendations and customer satisfaction.

Keywords

Sentiment Analysis NLTK VADER RoBERTa Transformers Flipkart Reviews

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