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

Authors

  • Rizki Annas Sholehat Telkom University, Bandung
  • Erwin Budi Setiawan Telkom University, Bandung
  • Yuliant Sibaroni Telkom University, Bandung

DOI:

https://doi.org/10.30865/mib.v7i2.5636

Keywords:

Deep Learning, Bidirectional Long Short-Term Memory, GloVe, Sentiment Analysis, Aspect

Abstract

Twitter as one of the social media with the most users in the world, is often used as a medium for sharing opinions that can be positive or negative. Movie reviews containing many complex explanations and judgments will be challenging to classify. Therefore a sentiment analysis process based on aspects is needed to analyze the polarity of film review opinions based on predetermined aspects. This research aims to analyze the polarity of film review opinions based on aspects using the Bidirectional Long Short-Term Memory method and GloVe feature extraction. This study uses plot, acting, and director aspects with a total dataset of 17.247 data. Bidirectional Long Short-Term Memory is proven to produce relevant and accurate results for sentiment analysis with the greatest accuracy of 56,29% in the plot aspect, 87,07% in the acting aspect, and 85,55% in the director aspect. GloVe feature extraction is proven to increase the performance value of this research by up to 13,57% in the plot aspect, 4,16% in the acting aspect, and 10,48% in the director aspect.

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Published

2023-04-27

Issue

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

Section

Articles

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