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.

Downloads

Download data is not yet available.

References

Pristiyono, M. Ritonga, M. A. Al Ihsan, A. Anjar, and F. H. Rambe, “Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1088, no. 1, p. 012045, 2021, doi: 10.1088/1757-899x/1088/1/012045.

V. N. T. Le, S. Ahderom, and K. Alameh, “Performances of the lbp based algorithm over cnn models for detecting crops and weeds with similar morphologies,” Sensors (Switzerland), vol. 20, no. 8, pp. 1–18, 2020, doi: 10.3390/s20082193.

A. L. Fairuz, R. D. Ramadhani, and N. A. Tanjung, “Analisis Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial,” J. DINDA, vol. 1, no. 1, pp. 10–12, 2021.

M. A. Fauzi and S. Adinugroho, “Analisis Sentimen Pariwisata di Kota Malang Menggunakan Metode Naive Bayes dan Seleksi Fitur Query Expansion Ranking Image Processing View project Smart Wheelchair View project,” Researchgate.Net, vol. 2, no. 8, pp. 2766–2770, 2018.

F. Gregorio, G. González, C. Schmidt, and J. Cousseau, “Internet of Things,” in Signals and Communication Technology, 2020.

S. Hartanto, “Implementasi Fuzzy Rule Based System.” in Techsi,vol 9 no 2, pp. 103-117, 2017

N. K. Chauhan and K. Singh, “A review on conventional machine learning vs deep learning,” Int. Conf. Comput. Power Commun. Technol. GUCON 2018, pp. 347–352, 2019

P. Yohana, “Analisis Sentimen Vaksin Covid19 Menggunakan Naive Bayes,”, Skripsi, 2022.

M. Rizky, “Vaksin Covid-19 Menggunakan Metode Support Vector Machine Pada Media Sosial Twitter Covid-19”,Skripsi, 2021.

A. Arfan and L. ETP, “Perbandingan Algoritma Long Short-Term Memory dengan SVR Pada Prediksi Harga Saham di Indonesia,” Petir, vol. 13, no. 1, pp. 33–43, 2020,

Y. Astari and S. W. Rozaqi, “Analisis Sentimen Multi-Class pada Sosial Media menggunakan metode Long Short-Term Memory ( LSTM ),” vol. 4, no. 1, pp. 8–12, 2021.

P. F. Muhammad, R. Kusumaningrum, and A. Wibowo, “Sentiment Analysis Using Word2vec and Long Short-Term Memory (LSTM) for Indonesian Hotel Reviews,” Procedia Comput. Sci., vol. 179, pp. 728–735, 2021

R. Wardoyo, A. Musdholifah, G. Angga Pradipta and I. N. Hariyasa Sanjaya, "Weighted Majority Voting by Statistical Performance Analysis on Ensemble Multiclassifier," 2020 Fifth International Conference on Informatics and Computing (ICIC), 2020, pp. 1-8, doi: 10.1109/ICIC50835.2020.9288552.

C. J. E. Munthe, N. A. Hasibuan, and H. Hutabarat, “Penerapan Algoritma Text Mining Dan TF-RF Dalam Menentukan Promo Produk Pada Marketplace,” vol. 2, no. 3, pp. 110–115, 2022.

Y. Xie, L. Le, Y. Zhou, and V. V. Raghavan, Deep Learning for Natural Language Processing, vol. 38. 2018.

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
Abstract viewed = 938 times
PDF downloaded = 846 times