Sentiment Analysis Of Energy Independence Tweets Using Simple Recurrent Neural Network

Kurnia Muludi(1), Mohammad Surya Akbar(2*), Dewi Asiah Shofiana(3), Admi Syarif(4)

(1) University of Lampung
(2) University of Lampung
(3) University of Lampung
(4) University of Lampung
(*) Corresponding Author


Sentiment analysis is part of computational research that extracts textual data to obtain positive, or negative values related to a topic. In recent research, data are commonly acquired from social media, including Twitter, where users often provide their personal opinion about a particular subject. Energy independence was once a trending topic discussed in Indonesia, as the opinions are diverse, pros and cons, making it interesting to be analyzed. Deep learning is a branch of machine learning consisting of hidden layers of neural networks by applying non-linear transformations and high-level model abstractions in large databases. The recurrent neural network (RNN) is a deep learning method that processes data repeatedly, primarily suitable for handwriting, multi-word data, or voice recognition. This study compares three algorithms: Simple Neural Network, Bernoulli Naive Bayes, and Long Short-Term Memory (LSTM) in sentiment analysis using the energy independence data from Twitter. Based on the results, the Simple Recurrent Neural Network shows the best performance with an accuracy value of 78% compared to Bernoulli Naive Bayes value of 67% and LSTM with an accuracy value of 75%.


Keywords Sentiment Analysis; Simple RNN; LSTM; Bernoulli Naive Bayes; Energy Independence;


Sentiment Analysis; Simple RNN; LSTM; Bernoulli Naive Bayes; Energy Independence;

Full Text:



[1] ESDM, Indonesia Energy Out Look 2019. Jakarta : Sekretariat Jenderal Dewan Energi Nasional, 2019.

[2] L. Zhang, R. Ghosh., M. Dekhil, M. Hsu, and B. Liu, “Combining lexicon-based and learning-based methods for twitter sentiment analysis”, HP Laboratories Technical Report, HPL-2011-89, 2011.

[3] H. Soong, N. B. A. Jalil, R. K. Ayyasamy, and R. Akbar, “The essential of sentiment analysis and opinion mining in social media”, 2019 IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE), pp. 272–277, 2019,

[4] A. Novantirani, M. K. Sabariah, and V. Effendy, “Analisis Sentimen pada Twitter untuk Mengenai Penggunaan Transportasi Umum Darat Dalam Kota dengan Metode Support Vector Machine”, E-Proceeeding of Engineering, 2015.

[5] B. Liu, “Sentiment Analysis and Subjectivity in: Handbook of Natural Language Processing”, Handbook of Natural Language Processing, Second Edition, vol. 2, pp. 568, 2010.

[6] K. Saranya, and S. Jayanthy, “Onto-based sentiment classification using machine learning techniques”, Proceedings of 2017 International Conference on Innovations in Information, Embedded and Communication Systems, ICIIECS 2017, Vol. 2018-January, pp. 1–5. Institute of Electrical and Electronics Engineers Inc. 2018

[7] A. Karpathy, “The Unreasonable Effectiveness of Recurrent Neural Networks”, Web Page, pp. 1–28, 2015. Available

[8] R. Socher, A. Perelygin, J. Y. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank”. In EMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. pp. 1631-1642. Association for Computational Linguistics (ACL), 2013.

[9] I. Zulfa, and E. Winarko, “Sentimen Analisis Tweet Berbahasa Indonesia dengan Deep Belief Network”, IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 11(2), pp 187, 2017.

[10] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space”, In 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings, International Conference on Learning Representations, ICLR. pp. 1–12, 2013.

[11] T. Mikolov, M. Karafiát, L. Burget, J. Cernocký, and S. Khudanpur, “Recurrent neural network based language model”, Proceedings of the 11th Annual Conference of the International Speech Communication Association. INTERSPEECH 2010. (September), pp. 1045–1048, 2010.

[12] J. Schmidhuber, “Deep learning in neural networks”: An overview. Neural Networks, vol. 61, pp. 85-117, 2015. Available doi: 10.1016/j.neunet.2014.09.003

[13] G. Singh, B. Kumar, L. Gaur, and A. Tyagi, “Comparison between Multinomial and Bernoulli Naïve Bayes for Text Classification”, In 2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019. pp. 593–596. Institute of Electrical and Electronics Engineers Inc, 2019, Available

[14] J. D. Novaković, A. Veljović, S. S. Ilić, Željko Papić, and T. Milica, “Evaluation of Classification Models in Machine Learning”, Theory Appl. Math. Comput. Sci., vol. 7, no. 1, pp. Pages: 39 -, Apr. 2017.


Article Metrics

Abstract views : 2103 | views : 1731


  • There are currently no refbacks.

Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133 |

View My Stats1
View My Stats2