A Comprehensive Comparative Analysis of Sentiment Analysis Models: CNN-LSTM vs. Hierarchical Attention Network (HAN)

Author(s)	Ankan Deb, Aarya Balpande, Bhavika Bhojwani, Sanskar Zanwar
Country	India
Abstract	As the digital world continues to evolve, sentiment analysis plays an increasingly crucial role in deciphering public opinion and influencing a company's image. This research delves deep into a comparative analysis of two powerful sentiment analysis models: Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and Hierarchical Attention Network (HAN). Through a meticulously crafted study utilizing the vast Sentiment140 dataset, we evaluate their performance, analyze their architecture, and delve into their computational efficiency and processing time. Our focus remains unwavering - to identify the most effective model for predicting sentiment towards a company's image, a key factor in shaping its success in today's competitive landscape.
Keywords	Deep Learning, Sentiment Analysis
Field	Computer
Published In	Volume 5, Issue 6, November-December 2023
Published On	2023-12-23
Cite This	A Comprehensive Comparative Analysis of Sentiment Analysis Models: CNN-LSTM vs. Hierarchical Attention Network (HAN) - Ankan Deb, Aarya Balpande, Bhavika Bhojwani, Sanskar Zanwar - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.10923
DOI	https://doi.org/10.36948/ijfmr.2023.v05i06.10923
Short DOI	https://doi.org/gs98rv

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