Sentiment Analysis on Social Media Using Fasttext Feature Expansion and Recurrent Neural Network (RNN) with Genetic Algorithm Optimization

Authors

  • Inggit Restu Illahi Telkom University
  • Erwin Budi Setiawan Telkom University, Indonesia

DOI:

https://doi.org/10.21108/ijoict.v10i1.905

Keywords:

FastText, Genetic Algorithm, RNN, Sentiment analysis, TF-IDF

Abstract

Social media is a place to express opinions or feelings, both positive and negative. One of them is to express opinions or feelings about a topic that is currently being discussed. The number of opinions or sentiments related to a topic can be challenging to assess if it leans towards positivity or negativity. Therefore, Sentiment analysis is essential for examining the viewpoints or sentiments on the topic. In this study, 37,391 Twitter user comments on the 2024 Indonesian presidential election were tested. This research employs the RNN methodology, TF-IDF feature extraction, and FastText feature expansion utilizing an IndoNews corpus of as much as 142,545 data and using Genetic Algorithm optimization. The outcomes of this study yielded the highest accuracy when combining TF-IDF feature extraction with max 7000 features, FastText feature expansion on top 5 features, and implementing Genetic Algorithm optimization with a value of 82.72%, accuracy increased by 3.4% from the baseline.

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References

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Published

2024-06-29

How to Cite

Inggit Restu Illahi, & Setiawan, E. B. . (2024). Sentiment Analysis on Social Media Using Fasttext Feature Expansion and Recurrent Neural Network (RNN) with Genetic Algorithm Optimization. International Journal on Information and Communication Technology (IJoICT), 10(1), 78–89. https://doi.org/10.21108/ijoict.v10i1.905

Issue

Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024

Section

Data Science

License

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