Analisis Sentimen terhadap Opini Feminisme Menggunakan Metode Naive Bayes
Keywords:
feminism, sentiment analysis, support vector machine, naive bayes, opinion
AbstractThe perspective of the development of feminism centered on women around the world who wants to be free from pressure, oppression and inequality from men, continues to this day. Various public opinions about feminism are now contained in various social media. Long debates about criticism and support for feminism in equalizing women's position both in terms of intellect, and the role of women in making decisions. This research was conducted with the aim of looking at public sentiment based on opinions circulating on social media. Hashtags or hash tags related to feminism from social media are the main data that will be used to analyze public opinion sentiment about feminism and 600 data are obtained about feminism. The data obtained were separated into positive, negative and neutral opinions for analysis using Naïve Bayes (NB). The results of using the Naïve Bayes method obtained a recall value of 84%, precision 94% and Fi-Score of 86% with an accuracy of 88%. Through this research, the results of classification using the nave Bayes method in analyzing sentiment against feminist opinions have good performance. Downloads
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References
[1] Chauhan, P., Sharma, N., & Sikka, G. (2021). The emergence of social media data and sentiment analysis in election prediction. Journal of Ambient Intelligence and Humanized Computing, 12(2), 2601-2627. DOI: https://doi.org/10.1007/s12652-020-02423-y
[2] Hruska, J., & Maresova, P. (2020). Use of social media platforms among adults in the United States—behavior on social media. Societies, 10(1), 27. DOI: https://doi.org/10.3390/soc10010027 [3] Drus, Z., & Khalid, H. (2019). Sentiment analysis in social media and its application: Systematic literature review. Procedia Computer Science, 161, 707-714. DOI: https://doi.org/10.1016/j.procs.2019.11.174 [4] Sharma, D., Sabharwal, M., Goyal, V., & Vij, M. (2020). Sentiment analysis techniques for social media data: A review. In First international conference on sustainable technologies for computational intelligence (pp. 75-90). Springer, Singapore. DOI: https://doi.org/10.1007/978-981-15-0029-9_7 [5] Alarifi, A., Tolba, A., Al-Makhadmeh, Z., & Said, W. (2020). A big data approach to sentiment analysis using greedy feature selection with cat swarm optimization-based long short-term memory neural networks. The Journal of Supercomputing, 76(6), 4414-4429. DOI: https://doi.org/10.1007/s11227-018-2398-2 [6] Sharma, A., & Ghose, U. (2020). Sentimental analysis of twitter data with respect to general elections in india. Procedia Computer Science, 173, 325-334. DOI: https://doi.org/10.1016/j.procs.2020.06.038 [7] Zahoor, S., & Rohilla, R. (2020, August). Twitter sentiment analysis using machine learning algorithms: a case study. In 2020 International Conference on Advances in Computing, Communication & Materials (ICACCM) (pp. 194-199). IEEE. DOI: https://doi.org/10.1109/ICACCM50413.2020.9213011 [8] Elder, L., Greene, S., & Lizotte, M. K. (2021). Feminist and anti-feminist identification in the 21st century United States. Journal of Women, Politics & Policy, 42(3), 243-259. DOI: https://doi.org/10.1080/1554477X.2021.1929607 [9] Tamboukou, M. (2020). Women Workers’ Education. Handbook of Historical Studies in Education: Debates, Tensions, and Directions, 813-829. DOI: https://doi.org/10.1007/978-981-10-2362-0_48 [10] Rif’at, D. F., & Nurwahidin, N. (2022). Feminisme Dan Kesetaraan Gender Dalam Kajian Islam Kontemporer. Syntax Literate; Jurnal Ilmiah Indonesia, 7(1), 172-182. DOI: http://dx.doi.org/10.36418/syntax-literate.v7i1.6038 [11] Alexeyeff, K. (2020, May). Cinderella of the south seas? Virtuous victims, empowerment and other fables of development feminism. In Women's Studies International Forum (Vol. 80, p. 102368). Pergamon. DOI: https://doi.org/10.1016/j.wsif.2020.102368 [12] Yadav, P., Saville, N., Arjyal, A., Baral, S., Kostkova, P., & Fordham, M. (2021). A feminist vision for transformative change to disaster risk reduction policies and practices. International Journal of Disaster Risk Reduction, 54, 102026. DOI: https://doi.org/10.1016/j.ijdrr.2020.102026 [13] Cannon, C. (2020). On the importance of feminist theories: Gender, race, sexuality and IPV. In Intimate partner violence and the LGBT+ community (pp. 37-52). Springer, Cham. DOI: https://doi.org/10.1016/j.erss.2021.102005 [14] Pathak, A. R., Pandey, M., & Rautaray, S. (2021). Topic-level sentiment analysis of social media data using deep learning. Applied Soft Computing, 108, 107440. DOI: https://doi.org/10.1016/j.asoc.2021.107440 [15] Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134. DOI: https://doi.org/10.1016/j.knosys.2021.107134 [16] Priyadarshini, I., & Cotton, C. (2021). A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis. The Journal of Supercomputing, 77(12), 13911-13932. DOI: https://doi.org/10.1007/s11227-021-03838-w [17] Kumar, A., & Garg, G. (2020). Systematic literature review on context-based sentiment analysis in social multimedia. Multimedia tools and Applications, 79(21), 15349-15380. DOI: https://doi.org/10.1007/s11042-019-7346-5 [18] Singh, R., & Singh, R. (2021). Applications of sentiment analysis and machine learning techniques in disease outbreak prediction–A review. Materials Today: Proceedings. DOI: https://doi.org/10.1016/j.matpr.2021.04.356 [19] Jindal, K., & Aron, R. (2021). A systematic study of sentiment analysis for social media data. Materials today: proceedings. DOI: https://doi.org/10.1016/j.matpr.2021.01.048 [20] Soumya, S., & Pramod, K. V. (2020). Sentiment analysis of Villavicencio, C., Macrohon, J. J., Inbaraj, X. A., Jeng, J. H., & Hsieh, J. G. (2021). Twitter sentiment analysis towards covid-19 vaccines in the Philippines using naïve bayes. Information, 12(5), 204. DOI: https://doi.org/10.3390/info12050204 [21] Geetha, M. P., & Renuka, D. K. (2021). Improving the performance of aspect based sentiment analysis using fine-tuned Bert Base Uncased model. International Journal of Intelligent Networks, 2, 64-69. DOI: https://doi.org/10.1016/j.ijin.2021.06.005 [22] Hakim, S. N., Putra, A. J., & Khasanah, A. U. (2021). Sentiment analysis on myindihome user reviews using support vector machine and naive bayes classifier method. International Journal of Industrial Optimization, 2(2), 151. DOI: https://doi.org/10.12928/ijio.v2i2.4437 [23] Fitriana, F., Utami, E., & Al Fatta, H. (2021). Analisis Sentimen Opini Terhadap Vaksin Covid-19 pada Media Sosial Twitter Menggunakan Support Vector Machine dan Naive Bayes. Jurnal Komtika (Komputasi dan Informatika), 5(1), 19-25. DOI: https://doi.org/10.31603/komtika.v5i1.5185 [24] Muzaki, A., & Witanti, A. (2021). Sentiment analysis of the community in the twitter to the 2020 election in pandemic covid-19 by method naive bayes classifier. Jurnal Teknik Informatika (Jutif), 2(2), 101-107. DOI: https://doi.org/10.20884/1.jutif.2021.2.2.51 [25] Fitri, E. (2020). Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest Dan Support Vector Machine. Jurnal Transformatika, 18(1), 71-80. DOI: http://dx.doi.org/10.26623/transformatika.v18i1.2317 |
Published
2022-12-31
Section
Articles
How to Cite
Wahyuni, W. (2022). Analisis Sentimen terhadap Opini Feminisme Menggunakan Metode Naive Bayes. Jurnal Informatika Ekonomi Bisnis, 4(4), 148-153. https://doi.org/10.37034/infeb.v4i4.162
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