Sentiment Analysis of Text Memes: A Comparison among Supervised Machine Learning Methods

Endah Asmawati, Ahmad Saikhu, Daniel Siahaan

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

5 Citations (Scopus)

Abstract

Meme is a new form of content in social media. A meme contains sentiment towards a particular issue, product, person, or entity. Memes can be in the form of text, images, or images that contain text. Memes are entertaining, critical, sarcastic, and may even be political. Traditional sentiment analysis methods deal with text. This study compares the performance of four sentiment analysis methods when used on Indonesian meme in the form of text and images that contain text. Firstly, the extraction of text memes was carried out, followed by the classification of the extracted text memes using supervised machine learning methods, namely Naïve Bayes, Support Vector Machines, Decision Tree, and Convolutional Neural Networks. Based on the experimental results, sentiment analysis on meme text using the Naïve Bayes method produced the best results, with an accuracy of 65.4%.

Original languageEnglish
Title of host publicationProceedings - 9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
EditorsMochammad Facta, Mohammad Syafrullah, Munawar Agus Riyadi, Imam Much Ibnu Subroto, Irawan Irawan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages349-354
Number of pages6
ISBN (Electronic)9786239213558
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022 - Jakarta, Indonesia
Duration: 6 Oct 20227 Oct 2022

Publication series

NameInternational Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
Volume2022-October
ISSN (Print)2407-439X

Conference

Conference9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
Country/TerritoryIndonesia
CityJakarta
Period6/10/227/10/22

Keywords

  • memes
  • sentiment analysis
  • supervised machine learning

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