Sentiment analysis of the attorney general's office performance in handling corruption cases on twitter using naïve bayes classification algorithm
Article Sidebar
Published:
Sep 30, 2023
Keywords:
Attorney General's Office, Corruption, Naïve Bayes Classification, Sentiment Analysis, Twitter
Section
Computer Science
Statistics Article
Article View : 135 Times
Main Article Content
Abstract
Corruption is defined as illegal activities such as bribery, fraud and forgery carried out through the abuse of power by public or private officials for personal, financial or other merits. In Indonesia, the Attorney General's Office is one of the institutions that has the authority to handle corruption cases. As the result of the overall business process, public perception is very important. One method to assess public perception is using data collected from social media. Among the many social media, Twitter is known for its high public interaction which then can be used to describe direct people's perceptions. This research aims to create a machine learning model using the Naïve Bayes Classification Algorithm based on Twitter data to determine public sentiment on the Attorney General's Office performance in handling corruption cases. As for the results, we managed to create a model with accuracy, recall, precision, and f-measure values of 74.34%, 71.80%, 73.09%, and 72.44% respectively. From the sentiment analysis result, it can be concluded that the public gives more positive sentiment to the Attorney General's Office in handling corruption cases carried out in the period January 2022 to December 2022 with a percentage of positive sentiment is 61.32% and 38.68% for the negative sentiment.
Article Details
How to Cite
Hermawan, Y. ., & Achsan, H. T. (2023). Sentiment analysis of the attorney general’s office performance in handling corruption cases on twitter using naïve bayes classification algorithm. Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA), 4(1), 49–56. https://doi.org/10.54076/jumpa.v3i2.329
References
Adams, J., Khan, H. T. A., Raeside, R., & White, D. I. (2007). Research methods for graduate business and social science students. SAGE publications India.
Andika, L. A., Azizah, P. A. N., & Respatiwulan, R. (2019). Analisis Sentimen Masyarakat terhadap Hasil Quick Count Pemilihan Presiden Indonesia 2019 pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier. Indonesian Journal of Applied Statistics, 2(1), 34–41.
Ariffin, K. A. Z., & Ahmad, F. H. (2021). Indicators for maturity and readiness for digital forensic investigation in era of industrial revolution 4.0. Computers & Security, 105, 102237.
Azmin, S., & Dhar, K. (2019). Emotion detection from bangla text corpus using naive bayes classifier. 2019 4th International Conference on Electrical Information and Communication Technology (EICT), 1–5.
Bahoo, S., Alon, I., & Paltrinieri, A. (2020). Corruption in international business: A review and research agenda. International Business Review, 29(4), 101660.
Berrar, D. (2018). Bayes’ theorem and naive Bayes classifier. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 403, 412.
Berry, M. W., & Kogan, J. (2010). Text mining: applications and theory. John Wiley & Sons.
Bhalla, R. (2022). A review paper on the role of sentiment analysis in quality education. SN Computer Science, 3(6), 469.
Bilal, M., Israr, H., Shahid, M., & Khan, A. (2016). Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques. Journal of King Saud University-Computer and Information Sciences, 28(3), 330–344.
Bošnjak, M., Oliveira, E., Martins, J., Mendes Rodrigues, E., & Sarmento, L. (2012). Twitterecho: a distributed focused crawler to support open research with twitter data. Proceedings of the 21st International Conference on World Wide Web, 1233–1240.
Cambria, E., Das, D., Bandyopadhyay, S., & Feraco, A. (2017). A practical guide to sentiment analysis.
Carr, C. T., & Hayes, R. A. (2015). Social media: Defining, developing, and divining. Atlantic Journal of Communication, 23(1), 46–65.
Ceron, A., Curini, L., Iacus, S. M., & Porro, G. (2014). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 16(2), 340–358.
Drus, Z., & Khalid, H. (2019). Sentiment analysis in social media and its application: Systematic literature review. Procedia Computer Science, 161, 707–714.
Dwi Riyanto, A. (2022). Hootsuite (We Are Social): Indonesian Digital Report 2022. Tersedia: https://andi. link/hootsuite-we-are-social-indonesian ….
Fajar, M., & Azhar, Z. (2019). Indeks persepsi korupsi dan pembangunan manusia terhadap pertumbuhan ekonomi di negara-negara asia tenggara. Jurnal Ecogen, 1(3), 681–690.
Ghosh, M., & Sanyal, G. (2018). An ensemble approach to stabilize the features for multi-domain sentiment analysis using supervised machine learning. Journal of Big Data, 5, 1–25.
Gunawan, D., Riana, D., Ardiansyah, D., Akbar, F., & Alfarizi, S. (2020). Komparasi Algoritma Support Vector Machine Dan Naïve Bayes Dengan Algoritma Genetika Pada Analisis Sentimen Calon Gubernur Jabar 2018-2023. V (1), 135–138.
Han, J., & Kamber, M. (2006). Data Mining Concepts and Techniques (A. Stephan, Ed.), 2nd edn., vol. 40. Morgan Kaufmann Publishers Inc and Elsevier Inc., San Francisco.
Haqqizar, N., & Larasyanti, T. N. (2019). Analisis Sentimen Terhadap Layanan Provider Telekomunikasi Telkomsel Di Twitter Dengan Metode Naïve Bayes. Prosiding TAU SNAR-TEK Seminar Nasional Rekayasa Dan Teknologi, 1(1), 30–33.
Hofmann, M., & Klinkenberg, R. (2016). RapidMiner: Data mining use cases and business analytics applications. CRC Press.
Hu, X., & Liu, H. (2012). Text analytics in social media. Mining Text Data, 385–414.
Kibriya, A. M., Frank, E., Pfahringer, B., & Holmes, G. (2005). Multinomial naive bayes for text categorization revisited. AI 2004: Advances in Artificial Intelligence: 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, 2004. Proceedings 17, 488–499.
Li, N., & Wu, D. D. (2010). Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Systems, 48(2), 354–368.
Mäntylä, M. V, Graziotin, D., & Kuutila, M. (2018). The evolution of sentiment analysis—A review of research topics, venues, and top cited papers. Computer Science Review, 27, 16–32.
Mursid, Z. (2019). ANALISIS SENTIMEN DI MEDIA SOSIAL TWITTER TERHADAP BRAND XIAOMI DENGAN ALGORITMA NAÏVE BAYES CLASSIFIER. Universitas AMIKOM Yogyakarta.
Onan, A. (2021). Sentiment analysis on massive open online course evaluations: a text mining and deep learning approach. Computer Applications in Engineering Education, 29(3), 572–589.
Paltrinieri, N., Comfort, L., & Reniers, G. (2019). Learning about risk: Machine learning for risk assessment. Safety Science, 118, 475–486.
Rafiq, A. (2020). Dampak media sosial terhadap perubahan sosial suatu masyarakat. Global Komunika, 1(1), 18–29.
Rohman, M. S. (2021). Partisipasi publik dalam transparansi penanganan tindak pidana korupsi oleh kpk melalui pemanfaatan media elektronik. Oetoesan-Hindia: Telaah Pemikiran Kebangsaan, 3(2), 72–87.
Rulli, N. (2017). Media sosial perspektif komunikasi, budaya, dan sosioteknologi. Bandung: Simbiosa Rekatama.
S, D. (2023). Number of social network users in selected countries in 2022 and 2027. Statista. https://www.statista.com/: https://www.statista.com/statistics/278341/number-of-social-network-users-in-selected-countries/
Samsir, S., Ambiyar, A., Verawardina, U., Edi, F., & Watrianthos, R. (2021). Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes. Jurnal Media Informatika Budidarma, 5(1), 157–163.
Santosa, B., & Umam, A. (2018). Data Mining dan Big Data Analytics: Teori dan Implementasi Menggunakan Python & Apache Spark. Penebar Media Pustaka.
Sloan, L., Morgan, J., Burnap, P., & Williams, M. (2015). Who tweets? Deriving the demographic characteristics of age, occupation and social class from Twitter user meta-data. PloS One, 10(3), e0115545.
Sutton, J., & Austin, Z. (2015). Qualitative research: Data collection, analysis, and management. The Canadian Journal of Hospital Pharmacy, 68(3), 226.
Tang, D., Qin, B., & Liu, T. (2015). Deep learning for sentiment analysis: successful approaches and future challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(6), 292–303.
Wahyuningrum, K. S., Disemadi, H. S., & Jaya, N. S. P. (2020). Independensi Komisi Pemberantasan Korupsi: Benarkah Ada? Refleksi Hukum: Jurnal Ilmu Hukum, 4(2), 239–258.
Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253.
Andika, L. A., Azizah, P. A. N., & Respatiwulan, R. (2019). Analisis Sentimen Masyarakat terhadap Hasil Quick Count Pemilihan Presiden Indonesia 2019 pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier. Indonesian Journal of Applied Statistics, 2(1), 34–41.
Ariffin, K. A. Z., & Ahmad, F. H. (2021). Indicators for maturity and readiness for digital forensic investigation in era of industrial revolution 4.0. Computers & Security, 105, 102237.
Azmin, S., & Dhar, K. (2019). Emotion detection from bangla text corpus using naive bayes classifier. 2019 4th International Conference on Electrical Information and Communication Technology (EICT), 1–5.
Bahoo, S., Alon, I., & Paltrinieri, A. (2020). Corruption in international business: A review and research agenda. International Business Review, 29(4), 101660.
Berrar, D. (2018). Bayes’ theorem and naive Bayes classifier. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, 403, 412.
Berry, M. W., & Kogan, J. (2010). Text mining: applications and theory. John Wiley & Sons.
Bhalla, R. (2022). A review paper on the role of sentiment analysis in quality education. SN Computer Science, 3(6), 469.
Bilal, M., Israr, H., Shahid, M., & Khan, A. (2016). Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques. Journal of King Saud University-Computer and Information Sciences, 28(3), 330–344.
Bošnjak, M., Oliveira, E., Martins, J., Mendes Rodrigues, E., & Sarmento, L. (2012). Twitterecho: a distributed focused crawler to support open research with twitter data. Proceedings of the 21st International Conference on World Wide Web, 1233–1240.
Cambria, E., Das, D., Bandyopadhyay, S., & Feraco, A. (2017). A practical guide to sentiment analysis.
Carr, C. T., & Hayes, R. A. (2015). Social media: Defining, developing, and divining. Atlantic Journal of Communication, 23(1), 46–65.
Ceron, A., Curini, L., Iacus, S. M., & Porro, G. (2014). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 16(2), 340–358.
Drus, Z., & Khalid, H. (2019). Sentiment analysis in social media and its application: Systematic literature review. Procedia Computer Science, 161, 707–714.
Dwi Riyanto, A. (2022). Hootsuite (We Are Social): Indonesian Digital Report 2022. Tersedia: https://andi. link/hootsuite-we-are-social-indonesian ….
Fajar, M., & Azhar, Z. (2019). Indeks persepsi korupsi dan pembangunan manusia terhadap pertumbuhan ekonomi di negara-negara asia tenggara. Jurnal Ecogen, 1(3), 681–690.
Ghosh, M., & Sanyal, G. (2018). An ensemble approach to stabilize the features for multi-domain sentiment analysis using supervised machine learning. Journal of Big Data, 5, 1–25.
Gunawan, D., Riana, D., Ardiansyah, D., Akbar, F., & Alfarizi, S. (2020). Komparasi Algoritma Support Vector Machine Dan Naïve Bayes Dengan Algoritma Genetika Pada Analisis Sentimen Calon Gubernur Jabar 2018-2023. V (1), 135–138.
Han, J., & Kamber, M. (2006). Data Mining Concepts and Techniques (A. Stephan, Ed.), 2nd edn., vol. 40. Morgan Kaufmann Publishers Inc and Elsevier Inc., San Francisco.
Haqqizar, N., & Larasyanti, T. N. (2019). Analisis Sentimen Terhadap Layanan Provider Telekomunikasi Telkomsel Di Twitter Dengan Metode Naïve Bayes. Prosiding TAU SNAR-TEK Seminar Nasional Rekayasa Dan Teknologi, 1(1), 30–33.
Hofmann, M., & Klinkenberg, R. (2016). RapidMiner: Data mining use cases and business analytics applications. CRC Press.
Hu, X., & Liu, H. (2012). Text analytics in social media. Mining Text Data, 385–414.
Kibriya, A. M., Frank, E., Pfahringer, B., & Holmes, G. (2005). Multinomial naive bayes for text categorization revisited. AI 2004: Advances in Artificial Intelligence: 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, 2004. Proceedings 17, 488–499.
Li, N., & Wu, D. D. (2010). Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Systems, 48(2), 354–368.
Mäntylä, M. V, Graziotin, D., & Kuutila, M. (2018). The evolution of sentiment analysis—A review of research topics, venues, and top cited papers. Computer Science Review, 27, 16–32.
Mursid, Z. (2019). ANALISIS SENTIMEN DI MEDIA SOSIAL TWITTER TERHADAP BRAND XIAOMI DENGAN ALGORITMA NAÏVE BAYES CLASSIFIER. Universitas AMIKOM Yogyakarta.
Onan, A. (2021). Sentiment analysis on massive open online course evaluations: a text mining and deep learning approach. Computer Applications in Engineering Education, 29(3), 572–589.
Paltrinieri, N., Comfort, L., & Reniers, G. (2019). Learning about risk: Machine learning for risk assessment. Safety Science, 118, 475–486.
Rafiq, A. (2020). Dampak media sosial terhadap perubahan sosial suatu masyarakat. Global Komunika, 1(1), 18–29.
Rohman, M. S. (2021). Partisipasi publik dalam transparansi penanganan tindak pidana korupsi oleh kpk melalui pemanfaatan media elektronik. Oetoesan-Hindia: Telaah Pemikiran Kebangsaan, 3(2), 72–87.
Rulli, N. (2017). Media sosial perspektif komunikasi, budaya, dan sosioteknologi. Bandung: Simbiosa Rekatama.
S, D. (2023). Number of social network users in selected countries in 2022 and 2027. Statista. https://www.statista.com/: https://www.statista.com/statistics/278341/number-of-social-network-users-in-selected-countries/
Samsir, S., Ambiyar, A., Verawardina, U., Edi, F., & Watrianthos, R. (2021). Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes. Jurnal Media Informatika Budidarma, 5(1), 157–163.
Santosa, B., & Umam, A. (2018). Data Mining dan Big Data Analytics: Teori dan Implementasi Menggunakan Python & Apache Spark. Penebar Media Pustaka.
Sloan, L., Morgan, J., Burnap, P., & Williams, M. (2015). Who tweets? Deriving the demographic characteristics of age, occupation and social class from Twitter user meta-data. PloS One, 10(3), e0115545.
Sutton, J., & Austin, Z. (2015). Qualitative research: Data collection, analysis, and management. The Canadian Journal of Hospital Pharmacy, 68(3), 226.
Tang, D., Qin, B., & Liu, T. (2015). Deep learning for sentiment analysis: successful approaches and future challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(6), 292–303.
Wahyuningrum, K. S., Disemadi, H. S., & Jaya, N. S. P. (2020). Independensi Komisi Pemberantasan Korupsi: Benarkah Ada? Refleksi Hukum: Jurnal Ilmu Hukum, 4(2), 239–258.
Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253.
Copyright and Licensing
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.