Aspect Based Sentiment Analysis with FastText Feature Expansion and
Support Vector Machine Method on Twitter
Dublin Core
Title
Aspect Based Sentiment Analysis with FastText Feature Expansion and
Support Vector Machine Method on Twitter
Support Vector Machine Method on Twitter
Subject
: twitter, aspect-based sentiment analysis, feature expansion, fasttext, svm
Description
Social media such as Twitter has now become very close to society. Twitter users can express current issues, their opinions,
product reviews, and many other things both positive and negative. Twitter is also used by companies to monitor the assessment
of their products among the public as insight that will be used to evaluate what aspects of their products need to be further
developed. Twitter with its limitation of only allowing users to post a maximum tweet of 280 characters will make a lot of
abbreviated and difficult to understand words used, so it will allow vocabulary mismatch problems to occur. Therefore, in this
paper, research conducted on aspect-based sentiment analysis of Telkomsel’s products from the aspects of signal and service
by applying feature expansion using Fasttext word embedding to overcome vocabulary mismatch problem and classification
with the Support Vector Machine (SVM) method. Sampling technique with Synthetic Minority Oversampling Technique
(SMOTE) used to overcome data imbalance. The experimental results show that feature expansion can increase the
performance of model. The final results obtained F1-Score value of the model for the signal aspect increased by 27.91% with
F1-Score 95.93%, and for the service aspect increased by 42.36% with F1-Score 94.53%.
product reviews, and many other things both positive and negative. Twitter is also used by companies to monitor the assessment
of their products among the public as insight that will be used to evaluate what aspects of their products need to be further
developed. Twitter with its limitation of only allowing users to post a maximum tweet of 280 characters will make a lot of
abbreviated and difficult to understand words used, so it will allow vocabulary mismatch problems to occur. Therefore, in this
paper, research conducted on aspect-based sentiment analysis of Telkomsel’s products from the aspects of signal and service
by applying feature expansion using Fasttext word embedding to overcome vocabulary mismatch problem and classification
with the Support Vector Machine (SVM) method. Sampling technique with Synthetic Minority Oversampling Technique
(SMOTE) used to overcome data imbalance. The experimental results show that feature expansion can increase the
performance of model. The final results obtained F1-Score value of the model for the signal aspect increased by 27.91% with
F1-Score 95.93%, and for the service aspect increased by 42.36% with F1-Score 94.53%.
Creator
Muhammad Afif Raihan1
, Erwin Budi Setiawan2
, Erwin Budi Setiawan2
Publisher
Telkom University
Date
: 22-08-2022
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Muhammad Afif Raihan1
, Erwin Budi Setiawan2, “Aspect Based Sentiment Analysis with FastText Feature Expansion and
Support Vector Machine Method on Twitter,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9215.
Support Vector Machine Method on Twitter,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9215.