Publication Type : Conference Proceedings
Publisher : IEEE
Source : 2025 Second International Conference on Networks and Soft Computing (ICNSoC)
Url : https://doi.org/10.1109/icnsoc66817.2025.00086
Campus : Amaravati
School : School of Computing
Department : Computer Science and Engineering
Year : 2025
Abstract : Movies significantly contribute to entertainment, inspiration, and an escape from reality. With the proliferation of online reviews, sentiment analysis facilitates a quick understanding of a film's reception. This study investigates sentiment classification using IMDb reviews, comparing Naïve Bayes models with Long Short-Term Memory (LSTM) networks. Specifically, Multinomial and Bernoulli Naïve Bayes classifiers were evaluated using Count Vectorizer and TF-IDF feature extraction techniques. The findings indicate that Bernoulli Naïve Bayes achieved an accuracy of 84.58%, while Multinomial Naïve Bayes attained 85.13%. However, the LSTM model outperformed both, reaching 88.17% accuracy. Beyond accuracy, LSTM also demonstrated superior precision
Cite this Research Publication : Kistam Gopi, Akkimsetti Somaraju, Gompa Viswanadh Naidu, Jeeru Bharath Reddy, Dontha Madhusudhana Rao, Exploring the Evolution of Sentiment Classification on Movie Reviews, 2025 Second International Conference on Networks and Soft Computing (ICNSoC), IEEE, 2025, https://doi.org/10.1109/icnsoc66817.2025.00086