Enhancing Sentiment Analysis with Hybrid Deep Learning Architectures

Vikash Sawan , Durga Prasad Roy

sentiment analysis; deep learning; Transformer; LSTM, SVM & ReLU.

Enhancing sentiment analysis on public opinion expressed in social networks, such as Twitter or Facebook, has been developed into a wide range of applications, but there are still many challenges to be addressed. Hybrid techniques have shown to be potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test the reliability of several hybrid techniques on various datasets of different domains. Our research questions are aimed at determining whether it is possible to produce hybrid models that outperform single models with different domains and types of datasets

Published Paper   E-Certificate

"Enhancing Sentiment Analysis with Hybrid Deep Learning Architectures", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.8, Issue 10, page no.661 - 672, October-2023, Available :https://ijsdr.org/papers/IJSDR2310109.pdf

Volume 8 Issue 10, October-2023

Pages : 661 - 672

Paper Reg. ID: IJSDR_208950

Published Paper Id: IJSDR2310109

Downloads: 000347200

Research Area: Computer Science & Technology 

Country: Mathura, Uttar Pradesh , India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2310109

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2310109

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition