Web-based Sentiment Analysis System Using SVM and TF-IDF with Statistical Feature

Muhammad Qois Huzyan Octava, Divi Galih Prasetyo Putri, Farhan Mufti Hilmy, Umar Farooq, Rachma Aurya Nurhaliza, Ganjar Alfian

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

Abstract

Social media's tendency for instant reactions can be harnessed by companies and organizations to gather feedback. Nevertheless, effectively analyzing vast amounts of social media data poses a challenge. This issue can be addressed through the use of sentiment analysis technology. In this study, a sentiment analysis model is developed, employing Support Vector Machine (SVM) and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms. The study aims to investigate the impact of feature engineering on TF-IDF, by incorporating statistical features into the SVM model's sentiment analysis performance. The experimental results reveal that the prediction model utilizing the conventional TFIDF approach achieves an SVM model with an F-measure score of 84.55%. Through the implementation of feature engineering, by adding max, min, and sum features, the model's performance shows a noticeable improvement, with an increase of 0.65% in the F-measure score difference. Consequently, the proposed feature engineering method positively enhances the capability of the SVM-based sentiment analysis model. To facilitate the acquisition of sentiment analysis results through user interfaces, the trained SVM model is integrated into a web-based sentiment analysis application. By doing so, the findings of this study contribute to streamlining the process of obtaining sentiment analysis results from social media data.
Original languageEnglish
Title of host publication 2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)
PublisherIEEE
Pages9-14
Number of pages6
ISBN (Electronic)9798350307771
ISBN (Print)9798350307788
DOIs
Publication statusE-pub ahead of print - 19 Jan 2024
Event2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies - Sakheer, Bahrain
Duration: 20 Nov 202321 Nov 2023
https://iiict.uob.edu.bh/3ict23/

Publication series

Name2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)
PublisherIEEE
ISSN (Print)2770-7458
ISSN (Electronic)2770-7466

Conference

Conference2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies
Abbreviated title3ICT
Country/TerritoryBahrain
City Sakheer
Period20/11/2321/11/23
Internet address

Keywords

  • Machine Learning
  • SVM
  • Sentiment Analysis
  • TF-IDF
  • Text Classification

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Information Systems
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Web-based Sentiment Analysis System Using SVM and TF-IDF with Statistical Feature'. Together they form a unique fingerprint.

Cite this