DOI: https://doi.org/10.14710/jtsiskom.2022.14141

Evaluations of Emotion Analysis of Tweets using Bidirectional Long Short Term Memory and Conventional Machine Learning

Universitas Nusa Mandiri, Indonesia

Received: 29 Mar 2021; Revised: 17 Jun 2021; Accepted: 22 Feb 2022; Published: 30 Apr 2022.
Open Access Copyright (c) 2022 Aloysius Kurniawan Santoso, Aliyah Kurniasih, Bagus Dwi Wicaksono, Hilman F Pardede
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

Many ideas are contained in the social media twitter as a form of expression for an event. This review can be used to determine a person's emotions based on text data so that we can determine the next action in addressing and responding to that opinion. Emotion classification on twitter can be done by recognizing the tweet text pattern of the user. In this study, representing emotions using the BiLSTM model and the Conventional Machine Learning model. The amount of learning rate and the number of layers and the optimizer used and the number of epochs in the BiLSTM model can affect the accuracy results. In the conventional machine learning model, the K value of the KNN, the selection of the naive bayes model on probalistic, and the Decision Tree variation in the values of Max-depth, min-leaves, min-split will affect the results of the accuracy value. So that we get a good model for the classification of emotional sentiments based on text data from an opinion on the tweets page.

 

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Keywords: Tweet; Emotion; BiLSTM; Machine Learning

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Section: Original Research Articles
Language : EN
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  1. S. Al-Saaqa, H. Abdel-Nabi, and A. Awajan, “A Survey of Textual Emotion Detection,” 2018 8th Int. Conf. Comput. Sci. Inf. Technol. CSIT 2018, pp. 136–142, 2018
  2. L. A. Andika, P. A. N. Azizah, and R. Respatiwulan, “Analisis Sentimen Masyarakat terhadap Hasil Quick Count Pemilihan Presiden Indonesia 2019 pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier,” Indones. J. Appl. Stat., vol. 2, no. 1, pp. 34–41, 2019
  3. M. S. Hadna, P. I. Santosa, and W. W. Winarno, “Studi Literatur Tentang Perbandingan Metode Untuk Proses Analisis Sentimen Di Twitter,” Semin. Nas. Teknol. Inf. dan Komun., vol. 2016, no. Sentika, pp. 57–64, 2016
  4. K. Gao, H. Xu, C. Gao, H. Hao, J. Deng, and X. Sun, “Attention-Based BiLSTM Network with Lexical Feature for Emotion Classification,” Proc. Int. Jt. Conf. Neural Networks, vol. 2018-July, pp. 1–8, 2018
  5. J. Xie, B. Chen, X. Gu, F. Liang, and X. Xu, “Self-Attention-Based BiLSTM Model for Short Text Fine-Grained Sentiment Classification,” IEEE Access, vol. 7, pp. 180558–180570, 2019
  6. P. Sharma and A. K. Sharma, “Experimental investigation of automated system for twitter sentiment analysis to predict the public emotions using machine learning algorithms,” Mater. Today Proc., no. xxxx, pp. 1–9, 2020
  7. A. Mathur, P. Kubde, and S. Vaidya, “Emotional analysis using twitter data during pandemic situation: Covid-19,” Proc. 5th Int. Conf. Commun. Electron. Syst. ICCES 2020, no. Icces, pp. 845–848, 2020
  8. A. F. Hidayatullah, S. Cahyaningtyas, and A. M. Hakim, “Sentiment Analysis on Twitter using Neural Network: Indonesian Presidential Election 2019 Dataset,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1077, no. 1, pp. 1–7, 2021
  9. S. Merin, “Twitter Reviews for Emotion Analysis,” 2020. [Online]. Available: https://www.kaggle.com/shainy/twitter-reviews-for-emotion-analysis/metadata
  10. A. R. Isnain, A. Sihabuddin, and Y. Suyanto, “Bidirectional Long Short Term Memory Method and Word2vec Extraction Approach for Hate Speech Detection,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 14, no. 2, pp. 169–178, 2020
  11. Y. Wang et al., “A comparison of word embeddings for the biomedical natural language processing,” J. Biomed. Inform., vol. 87, pp. 1–22, 2018
  12. M. Polignano, M. De Gemmis, P. Basile, and G. Semeraro, “A comparison of Word-Embeddings in Emotion Detection from Text using BiLSTM, CNN and Self-Attention,” ACM UMAP 2019 Adjun. - Adjun. Publ. 27th Conf. User Model. Adapt. Pers., pp. 63–68, 2019
  13. J. Abdillah, I. Asror, and Y. F. A. Wibowo, “Emotion Classification of Song Lyrics using Bidirectional LSTM Method with GloVe Word Representation Weighting,” Resti J., vol. 4, no. 4, pp. 723–729, 2020
  14. H. F. Fadli and A. F. Hidayatullah, “Identifikasi Cyberbullying pada Media Sosial Twitter Menggunakan Metode LSTM dan BiLSTM,” Pros. Autom., 2019
  15. F. Masri, D. Saepudin, and D. Adytia, “Forecasting of Sea Level Time Series using Deep Learning RNN, LSTM, and BiLSTM, Case Study in Jakarta Bay, Indonesia,” e-Proceeding Eng., vol. 7, no. 2, pp. 8544–8551, 2020
  16. Y. Lai, “A Comparison of Traditional Machine Learning and Deep Learning in Image Recognition,” J. Phys. Conf. Ser., vol. 1314, no. 1, pp. 1–8, 2019
  17. Z. Weng, “From Conventional Machine Learning to AutoML,” J. Phys. Conf. Ser., vol. 1207, no. 1, pp. 1–10, 2019
  18. [18] N. K. Chauhan and K. Singh, “A Review on Conventional Machine Learning vs Deep Learning,” 2018 Int. Conf. Comput. Power Commun. Technol. GUCON 2018, pp. 347–352, 2019
  19. T. Takase, S. Oyama, and M. Kurihara, “Effective neural network training with adaptive learning rate based on training loss,” Neural Networks, vol. 101, pp. 68–78, 2018
  20. J. Mafeni Mase, P. Chapman, G. P. Figueredo, and M. Torres Torres, “A Hybrid Deep Learning Approach for Driver Distraction Detection,” Int. Conf. ICT Converg., vol. 2020-October, no. September, pp. 1–6, 2020
  21. A. N. Kasanah, M. Muladi, and U. Pujianto, “Penerapan Teknik SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Objektivitas Berita Online Menggunakan Algoritma KNN,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 2, pp. 196–201, 2019
  22. A. Rahman and M. S. Hossen, “Sentiment Analysis on Movie Review Data Using Machine Learning Approach,” 2019 Int. Conf. Bangla Speech Lang. Process. ICBSLP 2019, pp. 27–28, 2019
  23. K. Prameswari and E. B. Setiawan, “Analisis Kepribadian Melalui Twitter Menggunakan Metode Logistic Regression dengan Pembobotan TF-IDF dan AHP,” e-Proceeding Eng., vol. 6, no. 2, pp. 9667–9682, 2019

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