Citation

Ahmed Khan, Talha and Sadiq, Rehan and Shahid, Zeeshan and Alam, Muhammad Mansoor and Mohd Su'ud, Mazliham (2024) Sentiment Analysis using Support Vector Machine and Random Forest. Journal of Informatics and Web Engineering, 3 (1). pp. 67-75. ISSN 2821-370X

Text 598-Article Text-5740-2-10-20240216.pdf - Published Version Restricted to Repository staff only Download (693kB)

Abstract

Sentiment analysis, is commonly known as opinion mining, is a vital field in natural language processing (NLP) that claims to find out the sentiment or emotion expressed in a given text. This research paper demonstrates an exhaustive survey of sentiment analysis, focusing on the application of machine learning techniques. Comprehensive parametric literature review has been completed to determine the sentiment analysis using SVM and Random Forest. Additionally, the paper covers preprocessing techniques, feature extraction, model training, evaluation, and challenges encountered in sentiment analysis. The findings of this research contribute to a deeper understanding of sentiment analysis and provide insights into the effectiveness of machine learning approaches in this domain. Based on the results obtained, two machine learning algorithms named as Random Forest and SVM were evaluated based on their accuracy in a classification task. The Random Forest algorithm achieved an accuracy of 0.78564, while SVM outperformed it slightly with an accuracy of 0.80394. Both Random Forest and SVM have demonstrated their strengths in achieving respectable accuracies in the given classification task. These results suggest that SVM, with its slightly higher accuracy of 0.80394, may be a more suitable choice when accuracy is the primary concern. However, the basic configuration need and characteristics of the problem at hand should be considered when choosing the better algorithm with better results.

Item Type:	Article
Uncontrolled Keywords:	Sentiment Analysis, Machine Learning, Opinion Mining, Natural Language Processing, Preprocessing Techniques, Feature Extraction
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software
Divisions:	Others
Depositing User:	Mr. MUHAMMAD AZRUL MOSRI
Date Deposited:	02 Apr 2024 00:09
Last Modified:	02 Apr 2024 00:09
URII:	http://shdl.mmu.edu.my/id/eprint/12243

Downloads

Edit (login required)