ANALISIS SENTIMEN ARTIKEL BERITA PEMILU BERBASIS METODE KLASIFIKASI
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May 10, 2023
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The distribution of information in the form of online news is so massive in the wider community, that it is difficult to distinguish between haox news and positive news. So that a classification is needed regarding public sentiment about the implementation of elections using mainstream media news article data using 1064 dataset test data. The methods used in this study are the naive Bayes algorithm, the random forest algorithm, and the support vector machine algorithm. The test model uses smote where the performance results are carried out by the algorithm used using smote and not using smote, where random forest produces an accuracy of 91.88%, while without using a smote support vector machine it produces an accuracy of 92.05%.
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Fathir, Hariyadi, M. A., & Miftachul A, Y. (2023). ANALISIS SENTIMEN ARTIKEL BERITA PEMILU BERBASIS METODE KLASIFIKASI. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 4(2), 485-493. https://doi.org/10.35870/jimik.v4i2.220
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References
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Mohasseb, A., Bader-El-Den, M., Cocea, M. and Liu, H., 2018, July. Improving imbalanced question classification using structured smote based approach. In 2018 International Conference on Machine Learning and Cybernetics (ICMLC) (Vol. 2, pp. 593-597). IEEE. DOI: https://doi.org/10.1109/ICMLC.2018.8527028.
Al-Azani, S. and El-Alfy, E.S.M., 2017. Using word embedding and ensemble learning for highly imbalanced data sentiment analysis in short arabic text. Procedia Computer Science, 109, pp.359-366. DOI: https://doi.org/10.1016/j.procs.2017.05.365.
Brzozowska-Woś, M., 2020. Wpływ cyfrowej komunikacji marketingowej na angażowanie się w markę i współtworzenie jej wartości przez młodych konsumentów. Wydawnictwo Politechniki Gdańskiej.
Waworundeng, J.M.S., Sandag, G.A., Sahulata, R.A. and Rellely, G.D., 2022. Sentiment Analysis of Online Lectures Tweets using Naïve Bayes Classifier. CogITo Smart Journal, 8(2), pp.371-384. DOI: https://doi.org/10.31154/cogito.v8i2.414.371-384.
Sharma, P. and Moh, T.S., 2016, December. Prediction of Indian election using sentiment analysis on Hindi Twitter. In 2016 IEEE international conference on big data (big data) (pp. 1966-1971). IEEE. DOI: https://doi.org/10.1109/BigData.2016.7840818.
Yang, S. and Zhang, H., 2018. Text mining of Twitter data using a latent Dirichlet allocation topic model and sentiment analysis. International Journal of Computer and Information Engineering, 12(7), pp.525-529.
Younis, E.M., 2015. Sentiment analysis and text mining for social media microblogs using open source tools: an empirical study. International Journal of Computer Applications, 112(5).
Rane, A. and Kumar, A., 2018, July. Sentiment classification system of twitter data for US airline service analysis. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) (Vol. 1, pp. 769-773). IEEE. DOI: https://doi.org/10.1109/COMPSAC.2018.00114.
Demidova, L.A., Klyueva, I.A. and Pylkin, A.N., 2019. Hybrid approach to improving the results of the SVM classification using the random forest algorithm. Procedia Computer Science, 150, pp.455-461. DOI: https://doi.org/10.1016/j.procs.2019.02.077.
Rezapour, M., 2021. Sentiment classification of skewed shoppers' reviews using machine learning techniques, examining the textual features. Engineering Reports, 3(1), p.e12280. DOI: https://doi.org/10.1002/eng2.12280.
Bansal, B. and Srivastava, S., 2018. Sentiment classification of online consumer reviews using word vector representations. Procedia computer science, 132, pp.1147-1153. DOI: https://doi.org/10.1016/j.procs.2018.05.029.
Nayak, A. and Natarajan, D., 2016. Comparative study of naive Bayes, support vector machine and random forest classifiers in sentiment analysis of twitter feeds. International Journal of Advance Studies in Computer Science and Engineering (IJASCSE), 5(1), p.16.
Wang, J., Xu, M., Wang, H. and Zhang, J., 2006, November. Classification of imbalanced data by using the SMOTE algorithm and locally linear embedding. In 2006 8th international Conference on Signal Processing (Vol. 3). IEEE. DOI: https://doi.org/10.1109/ICOSP.2006.345752.
Sarakit, P., Theeramunkong, T. and Haruechaiyasak, C., 2015, August. Improving emotion classification in imbalanced YouTube dataset using SMOTE algorithm. In 2015 2nd International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/ICAICTA.2015.7335373.
Ramanathan, V. and Meyyappan, T., 2019, January. Twitter text mining for sentiment analysis on people’s feedback about Oman tourism. In 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/ICBDSC.2019.8645596.
Astuti, I.F., Widagdo, P.P., Tanro, M.L.R., Cahyadi, D. and Suntara, A.A., 2023, February. Sentiment analysis on land and forest fire management in Twitter using Naïve Bayes method. In AIP Conference Proceedings (Vol. 2482, No. 1, p. 140004). AIP Publishing LLC. DOI: https://doi.org/10.1063/5.0110588.
Vapnik, V.N., 2009. Statistics the elements of statistical learning. Math. Intell, 27(2), pp.83-85.
Russell, M.A., 2013. Mining the social web: data mining Facebook, Twitter, LinkedIn, Google+, GitHub, and more. " O'Reilly Media, Inc.".
[Xu, R., Chen, T., Xia, Y., Lu, Q., Liu, B. and Wang, X., 2015. Word embedding composition for data imbalances in sentiment and emotion classification. Cognitive Computation, 7, pp.226-240. DOI: https://doi.org/10.1007/s12559-015-9319-y.
Song, J., Huang, X., Qin, S. and Song, Q., 2016, June. A bi-directional sampling based on K-means method for imbalance text classification. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/ICIS.2016.7550920.
Gata, W., Amsury, F., Wardhani, N.K., Sugiyarto, I., Sulistyowati, D.N. and Saputra, I., 2019, April. Informative tweet classification of the earthquake disaster situation in indonesia. In 2019 5th International Conference on Computing Engineering and Design (ICCED) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/ICCED46541.2019.9161135.
Mohasseb, A., Bader-El-Den, M., Cocea, M. and Liu, H., 2018, July. Improving imbalanced question classification using structured smote based approach. In 2018 International Conference on Machine Learning and Cybernetics (ICMLC) (Vol. 2, pp. 593-597). IEEE. DOI: https://doi.org/10.1109/ICMLC.2018.8527028.
Al-Azani, S. and El-Alfy, E.S.M., 2017. Using word embedding and ensemble learning for highly imbalanced data sentiment analysis in short arabic text. Procedia Computer Science, 109, pp.359-366. DOI: https://doi.org/10.1016/j.procs.2017.05.365.