TY - GEN
T1 - Sentiment Analysis of Text Memes
T2 - 9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
AU - Asmawati, Endah
AU - Saikhu, Ahmad
AU - Siahaan, Daniel
N1 - Publisher Copyright:
© 2022 Institute of Advanced Engineering and Science (IAES).
PY - 2022
Y1 - 2022
N2 - 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%.
AB - 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%.
KW - memes
KW - sentiment analysis
KW - supervised machine learning
UR - http://www.scopus.com/inward/record.url?scp=85142692506&partnerID=8YFLogxK
U2 - 10.23919/EECSI56542.2022.9946506
DO - 10.23919/EECSI56542.2022.9946506
M3 - Conference contribution
AN - SCOPUS:85142692506
T3 - International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
SP - 349
EP - 354
BT - Proceedings - 9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
A2 - Facta, Mochammad
A2 - Syafrullah, Mohammad
A2 - Riyadi, Munawar Agus
A2 - Subroto, Imam Much Ibnu
A2 - Irawan, Irawan
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 October 2022 through 7 October 2022
ER -