• Wida Sofiya Informatics, Faculty of Informatics, Universitas Telkom, Indonesia
  • Erwin Budi Setiawan Informatics, Faculty of Informatics, Universitas Telkom, Indonesia
Keywords: granularity, GRU, sentiment analysis, social media, svm


Social media platforms are widely used to share opinions, leading to a large growth of text data on the internet. This data can be a key source of up-to-date and inclusive information by conducting sentiment analysis. Typically, sentiment analysis research classifies binary based on the polar values generated. However, this has its limitations, such as classifying sentences containing positive and negative expressions, leading to incorrect predictions. Fine-grained sentiment analysis provides more precise results by associating values with more than two classification targets. The objective of this study is to carry out sentiment analysis at a fine-grained level related to public policy in Indonesia using the GRU-SVM model with feature extraction and expansion techniques. However, sentiment analysis research still faces challenges in NLP. Deep learning have successfully overcome the challenges of traditional machine learning models in terms of efficiency and performance. This study proposes GRU-SVM model. GRU is used because it can adaptively control dependencies, making it more efficient in memory usage, while SVM is used as it is state-of-the-art in sentiment analysis. Result of the study show that the selection of word representation techniques, the addition of feature extraction techniques, datasets, data ratios, and feature expansion are crucial in the model testing process. The GRU-SVM model achieved the best performance with an accuracy of 96.02%. Overall, the results of this study demonstrate that the GRU-SVM method is effective in analyzing sentiments in Indonesian tweets.


Download data is not yet available.


Z. Chen et al., “Emoji-powered Sentiment and Emotion Detection from Software Developers’ Communication Data,” ACM Transactions on Software Engineering and Methodology, vol. 30, no. 2, Mar. 2021, doi: 10.1145/3424308.

T. W. Sagala, M. S. Saputri, R. Mahendra, and I. Budi, “Stock Price Movement Prediction Using Technical Analysis and Sentiment Analysis,” in ACM International Conference Proceeding Series, Jan. 2020, pp. 123–127. doi: 10.1145/3379310.3381045.

H. Jang, E. Rempel, I. Roe, P. Adu, G. Carenini, and N. Z. Janjua, “Tracking Public Attitudes Toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-Based Sentiment Analysis,” J Med Internet Res, vol. 24, no. 3, p. e35016, Mar. 2022, doi: 10.2196/35016.

J. Zheng, L. Zheng, and L. Yang, “Research and Analysis in Fine-grained Sentiment of Film Reviews Based on Deep Learning,” in Journal of Physics: Conference Series, Jul. 2019, vol. 1237, no. 2. doi: 10.1088/1742-6596/1237/2/022152.

M. Munikar, S. Shakya, and A. Shrestha, “Fine-grained Sentiment Classification using BERT,” arXiv preprint, Oct. 2019, [Online]. Available:

A. Soufan, “Deep learning for sentiment analysis of Arabic text,” in ACM International Conference Proceeding Series, Mar. 2019. doi: 10.1145/3333165.3333185.

N. C. Dang, M. N. Moreno-García, and F. de la Prieta, “Sentiment analysis based on deep learning: A comparative study,” Electronics (Switzerland), vol. 9, no. 3, Mar. 2020, doi: 10.3390/electronics9030483.

Y. Xing and C. Xiao, “A GRU Model for Aspect Level Sentiment Analysis,” in Journal of Physics: Conference Series, Sep. 2019, vol. 1302, no. 3. doi: 10.1088/1742-6596/1302/3/032042.

L. Li, L. Yang, and Y. Zeng, “Improving sentiment classification of restaurant reviews with attention-based bi-gru neural network,” Symmetry (Basel), vol. 13, no. 8, Aug. 2021, doi: 10.3390/sym13081517.

Y. Liu, J. Lu, J. Yang, and F. Mao, “Sentiment analysis for e-commerce product reviews by deep learning model of Bert-BiGRU-Softmax,” Mathematical Biosciences and Engineering, vol. 17, no. 6, pp. 7819–7837, Nov. 2020, doi: 10.3934/MBE.2020398.

A. Agarwal, P. Dey, and S. Kumar, “Sentiment Analysis using Modified GRU,” in ACM International Conference Proceeding Series, Aug. 2022, pp. 356–361. doi: 10.1145/3549206.3549270.

S. Boonmatham and P. Meesad, “Stock Price Analysis with Natural Language Processing and Machine Learning,” in ACM International Conference Proceeding Series, Jul. 2020. doi: 10.1145/3406601.3406652.

C. N. Dang, M. N. Moreno-García, and F. de La Prieta, “Hybrid Deep Learning Models for Sentiment Analysis,” Complexity, vol. 2021, 2021, doi: 10.1155/2021/9986920.

M. Zulqarnain, R. Ghazali, Y. M. M. Hassim, and M. Rehan, “Text classification based on gated recurrent unit combines with support vector machine,” International Journal of Electrical and Computer Engineering, vol. 10, no. 4, pp. 3734–3742, 2020, doi: 10.11591/ijece.v10i4.pp3734-3742.

A. F. M. Agarap, “A neural network architecture combining gated recurrent unit (GRU) and support vector machine (SVM) for intrusion detection in network traffic data,” in ACM International Conference Proceeding Series, Feb. 2018, pp. 26–30. doi: 10.1145/3195106.3195117.

H. Saleh, S. Mostafa, L. A. Gabralla, A. O. Aseeri, and S. El-Sappagh, “Enhanced Arabic Sentiment Analysis Using a Novel Stacking Ensemble of Hybrid and Deep Learning Models,” Applied Sciences (Switzerland), vol. 12, no. 18, Sep. 2022, doi: 10.3390/app12188967.

G. Balikas, S. Moura, and M. R. Amini, “Multitask Learning for Fine-Grained Twitter Sentiment Analysis,” in SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Aug. 2017, pp. 1005–1008. doi: 10.1145/3077136.3080702.

JustAnotherArchivist, “snscrape: A social networking service scraper in Python,” 2018.

M. Dong, Universitas Telkom, Chinese and Oriental Languages Information Processing Society, Institute of Electrical and Electronics Engineers. Indonesia Section. Computer Society Chapter, and Institute of Electrical and Electronics Engineers, “Proceedings of the 2018 International Conference on Asian Language Processing (IALP) : 15-17 November 2018, Telkom University, Bandung, Indonesia”.

N. Yusliani, R. Primartha, and M. D. Marieska, “Multiprocessing Stemming: A Case Study of Indonesian Stemming,” 2019. [Online]. Available:

F. Z. Tala, “A Study of Stemming Effects on Information Retrieval in Bahasa Indonesia.”

M. Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.” [Online]. Available:

L. Zhang, S. Wang, and B. Liu, “Deep Learning for Sentiment Analysis: A Survey.”

L. Yue, W. Chen, X. Li, W. Zuo, and M. Yin, “A survey of sentiment analysis in social media,” Knowl Inf Syst, vol. 60, no. 2, pp. 617–663, Aug. 2019, doi: 10.1007/s10115-018-1236-4.

J. Pennington, R. Socher, and C. D. Manning, “GloVe: Global Vectors for Word Representation.” [Online]. Available: http://nlp.

K. Cho et al., “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” Jun. 2014, [Online]. Available:

Z. Penghua and Z. Dingyi, “Bidirectional-GRU based on attention mechanism for aspect-level Sentiment Analysis,” in ACM International Conference Proceeding Series, 2019, vol. Part F148150, pp. 86–90. doi: 10.1145/3318299.3318368.

How to Cite