Aspect-Based Sentiment Analysis on Twitter Using Long Short-Term Memory Method

Authors

  • Siti Inayah Putri Telkom University, Bandung
  • Erwin Budi Setiawan Telkom University, Bandung
  • Yuliant Sibaroni Telkom University, Bandung

DOI:

https://doi.org/10.30865/mib.v7i2.5637

Keywords:

Aspect-Based Analysis Sentiment, Movie Review, LSTM, Fasttext, TF-IDF, SMOTE

Abstract

Twitter is one of the most popular social media among Indonesian people. Due to the high number of users and the intensity of their use, Twitter can also be used to dig up information related to a topic or product with sentiment analysis. One of the most frequently discussed topics on Twitter is related to movie reviews. Everyone's opinion of movie reviews can refer to different aspects. So, aspect-based sentiment analysis can be applied to movie reviews to get more optimal results. Aspect-based sentiment analysis is a solution to find out the opinions of Twitter users on movie reviews based on the aspects. In this study, a system for aspect-based sentiment analysis was built with a dataset of Indonesian language movie reviews consisting of 3 aspects: plot, acting, and director. The classification model uses Long Short-Term Memory (LSTM) method with the application of TF-IDF feature extraction, fastText feature expansion, and handling of imbalanced data using SMOTE. The results of this study for the plot aspect obtained an accuracy score of 74.86% and F1-score of 74.74%, the acting aspect obtained an accuracy score of 94.80% and F1-score of 94.74%, and the director aspect obtained an accuracy score of 94.02% and F1-score of 93.89%.

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Published

2023-04-27

Issue

Vol. 7 No. 2 (2023): April 2023

Section

Articles

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