Implementation of Sentiment Analysis and Social Network Analysis of Twitter User Opinions on Web-Based Political Issues
DOI:
https://doi.org/10.31963/elekterika.v21i2.5102Keywords:
Sentiment Analysis, LSTM, Social Network Analysis, Twitter, WebAbstract
The functionality of both sentiment analysis and social network analysis on social media such as Twitter or X can holds significant potential for understanding public opinion, especially in political-related issues. Despite its potential, there is currently lack of application that can provide these types of analyses. This research aimed to help to analyse public opinion in the field of political issues by using sentiment analysis and social network analysis techniques that can help to analyse the tendency of public opinion polarity on Twitter or X social media and to visualize the relationship of social media accounts with other social media accounts. The results obtained from this research are web applications that can help in problems related to sentiment analysis and network analysis using libraries from the Python programming language, and NodeJs. Based on the results of blackbox testing and technical testing, it can be concluded that the system that has been built can run in accordance with the predetermined design.References
[1] I. Izzulsyah, A. Nur Hidayah, and L. Saputra, “ANALISIS PENGGUNAAN MEDIA SOSIAL DI MASA PANDEMI (ANALYSIS OF SOCIAL MEDIA USE DURING PANDEMIC),” Jurnal Fraction, vol. 2, no. 1, p. 21, 2022.
[2] M. G. Undap, V. P. Rantung, and P. T. D. Rompas, “Analisis Sentimen Situs Pembajak Artikel Penelitian Menggunakan Metode Lexicon-Based.”
[3] S. M. Alzahrani, “Big Data analytics tools: Twitter API and spark,” in 2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021, Institute of Electrical and Electronics Engineers Inc., Mar. 2021. doi: 10.1109/WIDSTAIF52235.2021.9430205.
[4] XDevelopers, “Starting February 9, we will no longer support free access to the Twitter API, both v2 and v1.1. A paid basic tier will be available instead,” Twitter. Accessed: Apr. 21, 2023. [Online]. Available: https://x.com/XDevelopers/status/ 1621026986784337922
[5] F. T. Saputra, S. H. Wijaya, Y. Nurhadryani, and Defina, “Lexicon Addition Effect on Lexicon-Based of Indonesian Sentiment Analysis on Twitter,” in Proceedings - 2nd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020, pp. 136–141. doi: 10.1109/ICIMCIS51567.2020.9354269.
[6] W. Budiharto and M. Meiliana, “Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis,” J Big Data, vol. 5, no. 1, Dec. 2018, doi: 10.1186/s40537-018-0164-1.
[7] R. L. Mustofa and B. Prasetiyo, “Sentiment analysis using lexicon-based method with naive bayes classifier algorithm on #newnormal hashtag in twitter,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Jun. 2021. doi: 10.1088/1742-6596/1918/4/042155.
[8] S. Chakraborty, J. Banik, D. Chatterjee, and S. Addhya, “Study of Dependency on number of LSTM units for Character based Text Generation models,” 2020.
[9] D. Inayah et al., “Seminar Nasional Official Statistics 2020: Statistics in The New Normal: A Challenge of Big Data and Official Statistics IMPLEMENTASI SOCIAL NETWORK ANALYSIS DALAM PENYEBARAN INFORMASI VIRUS CORONA (COVID-19) DI TWITTER (Implementation Social Network Analysis in Distribution of Corona Virus (Covid-19) Information on Twitter).”
[10] S. W. Wiiava and I. Handoko, “Examining a Covid-19 Twitter Hashtag Conversation in Indonesia: A Social Network Analysis Approach,” in Proceedings of the 2021 15th International Conference on Ubiquitous Information Management and Communication, IMCOM 2021, Institute of Electrical and Electronics Engineers Inc., Jan. 2021. doi: 10.1109/IMCOM51814.2021.9377382
[11] L. C. Freeman, The development of social network analysis : a study in the sociology of science. Empirical Press, 2004.
[12] Y. Bassil, “A Simulation Model for the Waterfall Software Development Life Cycle,” 2012. [Online]. Available: http://iet-journals.org/archive/2012/may_vol_2_no_5/255895133318216.pdf
[13] Aceng Abdul Wahid, “Analisis Metode Waterfall Untuk Pengembangan Sistem Informasi,” Jurnal Ilmu-ilmu Informatika dan Manajemen STMIK, no. November, 2020.
[14] F. Koto and G. Y. Rahmaningtyas, “Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs,” in 2017 International Conference on Asian Language Processing (IALP), 2017, pp. 391–394. doi: 10.1109/IALP.2017.8300625
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.