LSTM (Long Short Term Memory) for Sentiment COVID-19 Vaccine Classification on Twitter

  • Miftahul Ihsan Universitas Islam Negeri Sultan Syarif Kasim
  • Benny Sukma Negara Universitas Islam Negeri Sultan Syarif Kasim
  • Surya Agustian Universitas Islam Negeri Sultan Syarif Kasim
Keywords: LSTM, Vaksin Covid-19, word2vec, word embeddings, klasifikasi sentiment

Abstract

           The implementation of the Covid-19 vaccination carried out by Indonesian government was ignited pros and contras among the public. Certainly, there will be pros and cons about the vaccination from the community. This attituded of pros and cons, which is also called sentiment, can influence people to accept or refuse to be vaccinated. Todays, people express their sentiment in social media in comments, post, or status. One of the methods used to detect sentiment on social media, whether positive or negative, is through a categorisation of text approach. This research provides a deep learning technique for sentiment classification on Twitter that uses Long Short Term Memory (LSTM), for positive, neutral and negative classes. The word2vec word embeddings was used as input, using the pretrained Bahasa Indonesia model from Wikipedia corpus. On the other hand, the topic-based word2vec model was also trained from the Covid-19 vaccination sentiment dataset which collected from Twitter. The data used after balanced is 2564 training data, 778 data validation data, and 400 test data with 1802 neutral data, 1066 negative data, and 566 positive data. The best results from various parameter processes give an F1-Score value of 54% on the test data, with an accuracy of 66%. The result of this research is a model that can classify sentiments with new sentences.

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Published
2022-05-31
How to Cite
Ihsan, M., Benny Sukma Negara, & Surya Agustian. (2022). LSTM (Long Short Term Memory) for Sentiment COVID-19 Vaccine Classification on Twitter. Digital Zone: Jurnal Teknologi Informasi Dan Komunikasi, 13(1), 79-89. https://doi.org/10.31849/digitalzone.v13i1.9950
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