A Literature Review : Enhancing Sentiment Analysis of Deep Learning Techniques Using Generative AI Model
DOI:
https://doi.org/10.32628/CSEIT24103204Keywords:
Sentiment Analysis, Generative AI, Deep Learning TechniqueAbstract
Sentiment analysis is possibly one of the most desirable areas of study within Natural Language Processing (NLP). Generative AI can be used in sentiment analysis through the generation of text that reflects the sentiment or emotional tone of a given input. The process typically involves training a generative AI model on a large dataset of text examples labeled with sentiments (positive, negative, neutral, etc.). Once trained, the model can generate new text based on the learned patterns, providing an automated way to analyze sentiments in user reviews, comments, or any other form of textual data. The main goal of this research topic is to identify the emotions as well as opinions of users or customers using textual means. Though a lot of research has been done in this area using a variety of models, sentiment analysis is still regarded as a difficult topic with a lot of unresolved issues. Slang terms, novel languages, grammatical and spelling errors, etc. are some of the current issues. This work aims to conduct a review of the literature by utilizing multiple deep learning methods on a range of data sets. Nearly 21 contributions, covering a variety of sentimental analysis applications, are surveyed in the current literature study. Initially, the analysis looks at the kinds of deep learning algorithms that are being utilized and tries to show the contributions of each work. Additionally, the research focuses on identifying the kind of data that was used. Additionally, each work's performance metrics and setting are assessed, and the conclusion includes appropriate research gaps and challenges. This will help in identifying the non-saturated application for which sentimental analysis is most needed in future studies.
Downloads
References
Dr. G. S. N. Murthy, Shanmukha Rao Allu, Bhargavi Andhavarapu, Mounika Bagadi, Mounika Belusonti, Text based Sentiment Analysis using LSTM, International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181,Vol. 9 Issue 05, May-2020 DOI: https://doi.org/10.17577/IJERTV9IS050290
Putra Fissabil Muhammada, Retno Kusumaningruma, Adi Wibowoa, Sentiment Analysis Using Word2vec And Long Short-Term Memory (LSTM) For Indonesian Hotel Reviews, Procedia Computer Science 179 (2021) 728–735 DOI: https://doi.org/10.1016/j.procs.2021.01.061
Priyank Sonkiya,Vikas Bajpai,Anukriti Bansal, Stock price prediction using BERT and GAN, arXiv:2107.09055v1 [q-fin.ST] 18 Jul 2021
Ru Yang, Maryam Edalati, Using GAN-based models to sentimental analysis on imbalanced datasets in education domain, arXiv:2108.12061v1 [cs.CL] 26 Aug 2021
Ishaani Priyadarshini, Chase Cotton, A novel LSTM–CNN–grid search based deep neural network for sentiment analysis, The Journal of Supercomputing (2021) 77:13911–13932 https://doi.org/10.1007/s11227-021-03838-w DOI: https://doi.org/10.1007/s11227-021-03838-w
Akana Chandra Mouli Venkata Srinivas, Ch.Satyanarayana, Ch.Divakar,Katikireddy Phani Sirisha, Sentiment Analysis using Neural Network and LSTM, IOP Conf. Series: Materials Science and Engineering 1074 (2021) 012007 https://doi:10.1088/1757-899X/1074/1/012007 DOI: https://doi.org/10.1088/1757-899X/1074/1/012007
Ali Shariq Imran , Ru Yang , Zenun Kastrati , Sher Muhammad Daudpota , Sarang Shaikh, The impact of synthetic text generation for sentiment analysis using GAN based models, Egyptian Informatics Journal 23 (2022) 547–557 DOI: https://doi.org/10.1016/j.eij.2022.05.006
Bilen B., Horasan F., “LSTM network based sentiment analysis for customer reviews”, Politeknik Dergisi, 25(3): 959-966, (2022). DOI: 10.2339/politeknik.844019 DOI: https://doi.org/10.2339/politeknik.844019
Gustavo H. de Rosa , Jo ao P. Papa, A Survey on Text Generation using Generative Adversarial Networks, arXiv:2212.11119v1 [cs.CL] 20 Dec 2022
Nusrat Jahan Prottasha , Abdullah As Sami , Md Kowsher , Saydul Akbar Murad ,Anupam Kumar Bairagi , Mehedi Masud and Mohammed Baz, Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning, Sensors 2022, 22, 4157. https://doi.org/10.3390/s22114157 DOI: https://doi.org/10.3390/s22114157
Amjad Iqbal , Rashid Amin , Javed Iqbal , Roobaea Alroobaea , Ahmed Binmahfoudh and Mudassar Hussain, Sentiment Analysis of Consumer Reviews Using Deep Learning, Sustainability 2022, 14, 10844. https://doi.org/10.3390/su141710844 DOI: https://doi.org/10.3390/su141710844
Ying Cao , Zhexing Sun , Ling Li and Weinan Mo, A Study of Sentiment Analysis Algorithms for Agricultural Product Reviews Based on Improved BERT Model, Symmetry 2022, 14, 1604. https://doi.org/10.3390/sym14081604 DOI: https://doi.org/10.3390/sym14081604
Ms. K. Chitra, Dr. G. Kavitha, Dr. P. Latchoumy, Penalty based Sentimental Text Generation Framework using Generative Adversarial Networks, International Conference on Automation, Computing and Renewable Systems (ICACRS) , 978-1-6654-6084-2/22/$31.00 ©2022 IEEE https:109/ICACRS55517.2022.10029135//doi: 10.1
Chenxi Tian, Yuliang Ma , Jared Cammon , Feng Fang, Yingchun Zhang , and Ming Meng, Dual-Encoder VAE-GAN With Spatiotemporal Features for Emotional EEG Data Augmentation, IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 31, 2023 DOI: https://doi.org/10.1109/TNSRE.2023.3266810
Yaowei Yue , Yun Peng ,and Duancheng Wang, Deep Learning Short Text Sentiment Analysis Based on Improved Particle Swarm Optimization, Electronics 2023, 12, 4119. https://doi.org/10.3390/electronics12194119 DOI: https://doi.org/10.3390/electronics12194119
Abayomi Bello, Sin-Chun Ng and Man-Fai Leung, A BERT Framework to Sentiment Analysis of Tweets, Sensors 2023, 23, 506. https://doi.org/10.3390/s23010506 DOI: https://doi.org/10.3390/s23010506
Megharani Patil, Hrishikesh Yadav, Mahendra Gawali, Jaya Suryawanshi, Jaikumar Patil, Anjali Yeole, Prathik Shetty, Jayesh Potlabattini, A Novel Approach to Fake News Detection Using Generative AI,International Journal of Intelligent Systems and Applications in Engineering IJISAE, 2024, 12(4s), 343–354
Stanislav Chumakova,, Anton Kovantseva, Anatoliy Surikov, Generative approach to Aspect Based Sentiment Analysis with GPT Language Models, Procedia Computer Science 229 (2023) 284–293 DOI: https://doi.org/10.1016/j.procs.2023.12.030
Xiang Deng,Vasilisa Bashlovkina,Feng Han,Simon Baumgartner,Michael Bendersky, LLMs to the Moon? Reddit Market Sentiment Analysis with Large Language Models, WWW ’23 Companion, April 30–May 04, 2023, Austin, TX, USA DOI: https://doi.org/10.1145/3543873.3587605
Sio Jurnalis Pipin1, Frans Mikael Sinaga, Sunaryo Winardi, Muhammad Noor Hakim, Sentiment Analysis Classification of ChatGPT on Twitter Big Data in Indonesia Using Fast R-CNN, JURNAL MEDIA INFORMATIKA BUDIDARMA Volume 7, Nomor 4, Oktober 2023, Page 2137-2148 https://DOI: 10.30865/mib.v7i4.6816
Konstantinos I. Roumeliotis , Nikolaos D. Tselikas , Dimitrios K. Nasiopoulos, LLMs in e-commerce: A comparative analysis of GPT and LLaMA models in product review evaluation, Natural Language Processing Journal 6 (2024) 100056 DOI: https://doi.org/10.1016/j.nlp.2024.100056
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
