TY - GEN
T1 - Analysis of Multi-Class Sentiment on Indonesian Twitter Using Support Vector Machine Classification Algorithm with Particle Swarm Optimization
AU - Rozy, Nurul Faizah
AU - Amini, Nurkhairi
AU - Hakiem, Nashrul
AU - Afrizal, Sandra Hakiem
N1 - Publisher Copyright:
© 2023 ACM.
PY - 2023/10/13
Y1 - 2023/10/13
N2 - With the rapid evolution of the internet, widespread interactions occur across diverse media platforms, including Twitter. Tweets shared on Twitter often need to be categorized into different sentiment polarities, often referred to as multi-class classification. This research aims to enhance the accuracy of sentiment classification utilizing the Support Vector Machine (SVM) algorithm through the application of Particle Swarm Optimization (PSO). PSO is employed to determine optimal hyperparameter values that maximize the performance of the SVM classification model. Two models were employed for testing: SVM classification alone and SVM classification integrated with PSO optimization. The accuracy of the SVM classification yielded 63.7%, while the combined SVM-PSO approach achieved 65.8%. Consequently, this approach led to a 2.1% accuracy enhancement following optimization through the PSO algorithm.
AB - With the rapid evolution of the internet, widespread interactions occur across diverse media platforms, including Twitter. Tweets shared on Twitter often need to be categorized into different sentiment polarities, often referred to as multi-class classification. This research aims to enhance the accuracy of sentiment classification utilizing the Support Vector Machine (SVM) algorithm through the application of Particle Swarm Optimization (PSO). PSO is employed to determine optimal hyperparameter values that maximize the performance of the SVM classification model. Two models were employed for testing: SVM classification alone and SVM classification integrated with PSO optimization. The accuracy of the SVM classification yielded 63.7%, while the combined SVM-PSO approach achieved 65.8%. Consequently, this approach led to a 2.1% accuracy enhancement following optimization through the PSO algorithm.
KW - Emotional polarity
KW - Indonesian Twitter
KW - Particle Swarm Optimization
KW - Sentiment analysis
KW - Support Vector Machine
UR - https://www.scopus.com/pages/publications/85184090393
U2 - 10.1145/3633598.3633609
DO - 10.1145/3633598.3633609
M3 - Conference contribution
AN - SCOPUS:85184090393
T3 - ACM International Conference Proceeding Series
SP - 62
EP - 67
BT - ICAAI 2023 - 2023 the 7th International Conference on Advances in Artificial Intelligence
PB - Association for Computing Machinery
T2 - 7th International Conference on Advances in Artificial Intelligence, ICAAI 2023
Y2 - 13 October 2023 through 15 October 2023
ER -