Article: Machine learning approaches to sentiment analysis in online social networks Journal: International Journal of Work Innovation (IJWI) 2023 Vol.3 No.4 pp.317 - 337 Abstract: The online social network presents the quantitative measure of the psychological behaviour of individuals and helps to analyse the generic standpoint of social or political issues. As the field of research in text mining, it follows a computational approach to determine the opinions, sentiments, and subjectivity of text and other expressions. Moreover, the majority of approaches try to model the syntactic information of words without considering sentiment. The present study gives a brief narration of different machine learning (ML) models used for sentiment analysis and also proposes an efficient modular approach to give precise accuracy in validating and testing the Twitter data. The objective is to solve the problems through evaluation and comparison of different methods based on accuracy and training time. The proposed model achieves an accuracy of 88.37% with minimum possible training time. Simulation study states an effective way in which dataset may be thoroughly analysed and implemented with a focus on further validation of sentiment dataset to make tweet sentiment analysis more accurate. Inderscience Publishers - linking academia, business and industry through research

Title: Machine learning approaches to sentiment analysis in online social networks

Authors: Chandrakant Mallick; Sarojananda Mishra; Parimal Kumar Giri; Bijay Kumar Paikaray

Addresses: Department of Computer Science and Engineering, Biju Patnaik University of Technology, Rourkela, Odisha, India ' Department of Computer Science and Engineering, IGIT Sarang, Odisha, India ' Department of Computer Science and Application, College of Engineering, Bhubaneswar, Odisha, India ' School of Information and Communication Technology, Medhavi Skills University, Sikkim, India

Abstract: The online social network presents the quantitative measure of the psychological behaviour of individuals and helps to analyse the generic standpoint of social or political issues. As the field of research in text mining, it follows a computational approach to determine the opinions, sentiments, and subjectivity of text and other expressions. Moreover, the majority of approaches try to model the syntactic information of words without considering sentiment. The present study gives a brief narration of different machine learning (ML) models used for sentiment analysis and also proposes an efficient modular approach to give precise accuracy in validating and testing the Twitter data. The objective is to solve the problems through evaluation and comparison of different methods based on accuracy and training time. The proposed model achieves an accuracy of 88.37% with minimum possible training time. Simulation study states an effective way in which dataset may be thoroughly analysed and implemented with a focus on further validation of sentiment dataset to make tweet sentiment analysis more accurate.

Keywords: sentiment analysis; regression analysis; neural network; machine learning; opinion mining; online social network.

DOI: 10.1504/IJWI.2023.128860

International Journal of Work Innovation, 2023 Vol.3 No.4, pp.317 - 337

Received: 27 Aug 2022
Accepted: 01 Oct 2022
Published online: 07 Feb 2023 *

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Title: Machine learning approaches to sentiment analysis in online social networks

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