Open Access

ARTICLE

SA-MSVM: Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

C. P. Thamil Selvi^1,*, R. PushpaLakshmi²

1 Department of Computer science and Engineering, Sri Ranganathar Institute of Engineering and Technology, Coimbatore, Tamilnadu, 641009, India
2 Department of Information and Technology, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, 624622, India

* Corresponding Author: C. P. Thamil Selvi. Email:

Computer Systems Science and Engineering 2023, 44(3), 2439-2456. https://doi.org/10.32604/csse.2023.029254

Received 28 February 2022; Accepted 01 April 2022; Issue published 01 August 2022

Abstract

One of the drastically growing and emerging research areas used in most information technology industries is Bigdata analytics. Bigdata is created from social websites like Facebook, WhatsApp, Twitter, etc. Opinions about products, persons, initiatives, political issues, research achievements, and entertainment are discussed on social websites. The unique data analytics method cannot be applied to various social websites since the data formats are different. Several approaches, techniques, and tools have been used for big data analytics, opinion mining, or sentiment analysis, but the accuracy is yet to be improved. The proposed work is motivated to do sentiment analysis on Twitter data for cloth products using Simulated Annealing incorporated with the Multiclass Support Vector Machine (SA-MSVM) approach. SA-MSVM is a hybrid heuristic approach for selecting and classifying text-based sentimental words following the Natural Language Processing (NLP) process applied on tweets extracted from the Twitter dataset. A simulated annealing algorithm searches for relevant features and selects and identifies sentimental terms that customers criticize. SA-MSVM is implemented, experimented with MATLAB, and the results are verified. The results concluded that SA-MSVM has more potential in sentiment analysis and classification than the existing Support Vector Machine (SVM) approach. SA-MSVM has obtained 96.34% accuracy in classifying the product review compared with the existing systems.

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Keywords

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