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A Comparative Study on Sentiment Analysis Using CNN, GRU and LSTM

Irene Serah Thomas, Renju Rachel Varghese, Dr. Nisha Joseph

Abstract


The surge in e-commerce underscores the pivotal role of customer reviews in shaping online shopping experiences. Sentiment analysis, a vital component of natural language processing, explores sentiments within text data. This study examines Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) for sentiment analysis using the Amazon Reviews dataset. CNNs focus on local patterns, while GRUs and LSTMs capture sequential dependencies. Results show superior accuracy and F1 scores in GRU compared to CNN and LSTM. This comparative analysis aims to empower customers by effectively interpreting sentiments in reviews, without explicitly providing recommendations.

 


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