Sentiment Analysis Terhadap Tulisan Mengenai Universitas Pertamina Di Media Sosial Twitter
Article Sidebar
Published:
Nov 28, 2022
Keywords:
sentiment analysis machine learning social media neural network
Main Article Content
Abstract
Nowadays, branding is not only applied to business sectors. Many universities have implemented branding to maintain the university's brand image so that it can attract prospective students. Sentiment analysis is one of the activities to monitor the university’s brand image in the community. This research applies a machine learning approach to conduct a social media analysis of Twitter. The dataset consists of 4323 tweets containing the word ”Universitas Pertamina”. Each tweet is then grouped into three different classes based on the tweet's sentiment, namely negative, neutral and positive. This study uses three types of machine learning models: Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and CNN-LSTM. The procedure for the three models uses the stratified 5-fold cross-validation technique. Model performance is then measured using a learning curve and measuring parameters such as accuracy, weighted-f1 and balanced accuracy. The CNN-LSTM model records the highest accuracy value, 76.92% and the highest weighted-f1 with 76.78%. Meanwhile, the LSTM model gets the highest balanced accuracy value with 66.05%.
Downloads
Download data is not yet available.
Article Details
How to Cite
Widyayulianto, M. R., Susanty, M., & Irawan, A. (2022). Sentiment Analysis Terhadap Tulisan Mengenai Universitas Pertamina Di Media Sosial Twitter. PETIR, 15(2), 276–286. https://doi.org/10.33322/petir.v15i2.1197
Section
Articles
References
[1] S. Gensler, F. Völckner, M. Egger, K. Fischbach, dan D. Schoder, “Listen to Your Customers: Insights into Brand Image Using Online Consumer-Generated Product Reviews,” International Journal of Electronic Commerce, vol. 20, no. 1, hlm. 112–141, Agu 2015, doi: 10.1080/10864415.2016.1061792.
[2] Y. Zhang, “The Impact of Brand Image on Consumer Behavior: A Literature Review,” Open Journal of Business and Management, vol. 3, no. 1, hlm. 58–62, 2015, doi: 10.4236/ojbm.2015.31006.
[3] D. Marsh dan P. Fawcett, “Branding, politics and democracy,” Policy Studies, vol. 32, no. 5, hlm. 515–530, Sep 2011, doi: 10.1080/01442872.2011.586498.
[4] L. Zhang and S. Zhao, "City branding and the Olympic effect: A case study of Beijing", Cities, vol. 26, no. 5, pp. 245-254, 2009. Available: 10.1016/j.cities.2009.05.002.
[5] S. Lambert, "Number of Social Media Users in 2020: Demographics & Predictions - Financesonline.com", Financesonline.com. [Daring]. Tersedia: https://financesonline.com/number-of-social-media-users/. [Diakses: 23- May- 2020].
[6] T. Mitchell, Machine learning. New York: McGraw Hill, 2017.
[7] B. Liu, "Sentiment Analysis and Subjectivity", Handbook of Natural Language Processing, 2nd ed., 2010.
[8] W. Medhat, A. Hassan, dan H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, vol. 5, no. 4, hlm. 1093–1113, Des 2014, doi: 10.1016/j.asej.2014.04.011.K. Gurney, An Introduction to Neural Networks, 1st ed. Taylor & Francis, Inc., 1997.
[9] S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," in Neural Computation, vol. 9, no. 8, pp. 1735-1780, 15 Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
[10] M. Valueva, N. Nagornov, P. Lyakhov, G. Valuev and N. Chervyakov, "Application of the residue number system to reduce hardware costs of the convolutional neural network implementation", Mathematics and Computers in Simulation, vol. 177, pp. 232-243, 2020. Tersedia: 10.1016/j.matcom.2020.04.031.
[11] M. S. Saputri, R. Mahendra, dan M. Adriani, “Emotion Classification on Indonesian Twitter Dataset,” dalam 2018 International Conference on Asian Language Processing (IALP), 2018, doi: 10.1109/ialp.2018.8629262.
[12] B. H. Iswanto dan V. Poerwoto, “Sentiment analysis on Bahasa Indonesia tweets using Unibigram models and machine learning techniques,” IOP Conference Series: Materials Science and Engineering, vol. 434, hlm. 12255, Des 2018.
[13] K. Gurney, An Introduction to Neural Networks, 1st ed. Taylor & Francis, Inc., 1997.
[14] W. S. McCulloch dan W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” The Bulletin of Mathematical Biophysics, vol. 5, no. 4, hlm. 115–133, Des 1943, doi: 10.1007/bf02478259.
[15] Y. Kim, “Convolutional Neural Networks for Sentence Classification,” dalam Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, doi: 10.3115/v1/d14-1181.
[16] A. Burkov, The hundred-page machine learning book. 2019.
[17] C. Zhou, C. Sun, Z. Liu, F.C.M. Lau, “A C-LSTM Neural Network for Text Classification”, 2015. [Daring]. Tersedia: arXiv:cond-mat/0303149. arXiv:1511.08630v2
[18] G. Varoquaux, L. Buitinck, G. Louppe, O. Grisel, F. Pedregosa and A. Mueller, "Scikit-learn", GetMobile: Mobile Computing and Communications, vol. 19, no. 1, pp. 29-33, 2015. Available: 10.1145/2786984.2786995.
[19] J.S. Akosa, "Predictive Accuracy: A Misleading Performance Measure for Highly Imbalanced Data", Proceedings of the SAS Global Forum, 2017.
[20] M. Anzanello and F. Fogliatto, "Learning curve models and applications: Literature review and research directions", International Journal of Industrial Ergonomics, vol. 41, no. 5, pp. 573-583, 2011. Tersedia: 10.1016/j.ergon.2011.05.001.
[21] J. Brownlee, "How to use Learning Curves to Diagnose Machine Learning Model Performance", Machine Learning Mastery, 2019. [Daring]. Tersedia: https://machinelearningmastery.com/learning-curves-for-diagnosing-machine-learning-model-performance/. [Diakses: 04- Jun- 2020].
[22] S. Mohammad, “A Practical Guide to Sentiment Annotation: Challenges and Solutions,” dalam Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2016, doi: 10.18653/v1/w16-0429.
[23] N. Aliyah Salsabila, Y. Ardhito Winatmoko, A. Akbar Septiandri, dan A. Jamal, “Colloquial Indonesian Lexicon,” dalam 2018 International Conference on Asian Language Processing (IALP), 2018, doi: 10.1109/ialp.2018.8629151.
[2] Y. Zhang, “The Impact of Brand Image on Consumer Behavior: A Literature Review,” Open Journal of Business and Management, vol. 3, no. 1, hlm. 58–62, 2015, doi: 10.4236/ojbm.2015.31006.
[3] D. Marsh dan P. Fawcett, “Branding, politics and democracy,” Policy Studies, vol. 32, no. 5, hlm. 515–530, Sep 2011, doi: 10.1080/01442872.2011.586498.
[4] L. Zhang and S. Zhao, "City branding and the Olympic effect: A case study of Beijing", Cities, vol. 26, no. 5, pp. 245-254, 2009. Available: 10.1016/j.cities.2009.05.002.
[5] S. Lambert, "Number of Social Media Users in 2020: Demographics & Predictions - Financesonline.com", Financesonline.com. [Daring]. Tersedia: https://financesonline.com/number-of-social-media-users/. [Diakses: 23- May- 2020].
[6] T. Mitchell, Machine learning. New York: McGraw Hill, 2017.
[7] B. Liu, "Sentiment Analysis and Subjectivity", Handbook of Natural Language Processing, 2nd ed., 2010.
[8] W. Medhat, A. Hassan, dan H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, vol. 5, no. 4, hlm. 1093–1113, Des 2014, doi: 10.1016/j.asej.2014.04.011.K. Gurney, An Introduction to Neural Networks, 1st ed. Taylor & Francis, Inc., 1997.
[9] S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," in Neural Computation, vol. 9, no. 8, pp. 1735-1780, 15 Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.
[10] M. Valueva, N. Nagornov, P. Lyakhov, G. Valuev and N. Chervyakov, "Application of the residue number system to reduce hardware costs of the convolutional neural network implementation", Mathematics and Computers in Simulation, vol. 177, pp. 232-243, 2020. Tersedia: 10.1016/j.matcom.2020.04.031.
[11] M. S. Saputri, R. Mahendra, dan M. Adriani, “Emotion Classification on Indonesian Twitter Dataset,” dalam 2018 International Conference on Asian Language Processing (IALP), 2018, doi: 10.1109/ialp.2018.8629262.
[12] B. H. Iswanto dan V. Poerwoto, “Sentiment analysis on Bahasa Indonesia tweets using Unibigram models and machine learning techniques,” IOP Conference Series: Materials Science and Engineering, vol. 434, hlm. 12255, Des 2018.
[13] K. Gurney, An Introduction to Neural Networks, 1st ed. Taylor & Francis, Inc., 1997.
[14] W. S. McCulloch dan W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” The Bulletin of Mathematical Biophysics, vol. 5, no. 4, hlm. 115–133, Des 1943, doi: 10.1007/bf02478259.
[15] Y. Kim, “Convolutional Neural Networks for Sentence Classification,” dalam Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, doi: 10.3115/v1/d14-1181.
[16] A. Burkov, The hundred-page machine learning book. 2019.
[17] C. Zhou, C. Sun, Z. Liu, F.C.M. Lau, “A C-LSTM Neural Network for Text Classification”, 2015. [Daring]. Tersedia: arXiv:cond-mat/0303149. arXiv:1511.08630v2
[18] G. Varoquaux, L. Buitinck, G. Louppe, O. Grisel, F. Pedregosa and A. Mueller, "Scikit-learn", GetMobile: Mobile Computing and Communications, vol. 19, no. 1, pp. 29-33, 2015. Available: 10.1145/2786984.2786995.
[19] J.S. Akosa, "Predictive Accuracy: A Misleading Performance Measure for Highly Imbalanced Data", Proceedings of the SAS Global Forum, 2017.
[20] M. Anzanello and F. Fogliatto, "Learning curve models and applications: Literature review and research directions", International Journal of Industrial Ergonomics, vol. 41, no. 5, pp. 573-583, 2011. Tersedia: 10.1016/j.ergon.2011.05.001.
[21] J. Brownlee, "How to use Learning Curves to Diagnose Machine Learning Model Performance", Machine Learning Mastery, 2019. [Daring]. Tersedia: https://machinelearningmastery.com/learning-curves-for-diagnosing-machine-learning-model-performance/. [Diakses: 04- Jun- 2020].
[22] S. Mohammad, “A Practical Guide to Sentiment Annotation: Challenges and Solutions,” dalam Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2016, doi: 10.18653/v1/w16-0429.
[23] N. Aliyah Salsabila, Y. Ardhito Winatmoko, A. Akbar Septiandri, dan A. Jamal, “Colloquial Indonesian Lexicon,” dalam 2018 International Conference on Asian Language Processing (IALP), 2018, doi: 10.1109/ialp.2018.8629151.