Sentiment Analysis on Twitter about the Use of City Public Transportation Using Support Vector Machine Method
DOI:
https://doi.org/10.21108/IJOICT.2016.21.85Abstract
Traffic jams that occur in big cities in Indonesia due to the increased use of private vehicles. One solution to overcome this problem is to increase the use of public transport. But, the existing public transport is still not much in demand by the community. Some people express their opinions regarding the use of city public transportation via Twitter. Â The opinions can be processed as a sentiment analysis to determine the positive opinions and negative opinions. The opinion will then be analyzed to determine factors that are the main cause of the ineligibility use of public transport as well as the factors that make the public choose to use this type of transport. By upgrading of facilities and services based on the results of sentiment analysis, it is expected that people will switch to use city public transportation, which would reduce the traffic jam. Â This research used SVM method to process sentiment analysis. The result has shown SVM accuracy reaches 78.12%, which indicates that the results of this reserach deserve to be considered.
Downloads
References
Direktur Jenderal Perhubungan Darat. (2002). Pedoman Teknis Penyelenggaraan Angkutan Penumpang Umum di Wilayah Perkotaan Dalam Trayek Tetap dan Teratur.
Furqan, & Sjafruddin, A. (2013, April 29). Retrieved November 1, 2013, from Prof. Ade Sjafruddin: Angkutan Umum, Solusi Kunci Kemacetan Jakarta: http://www.itb.ac.id/news/3899.xhtml
Go, A., Bhayani, R., & Huang, L. (2009). Twitter Sentiment Classification using Distant Supervision. Project Report, Stanford.
Han, J., & Kamber, M. (2006). Data Mining Concept and Technique. San Francisco: Elsevier.
Irmansyah, A. (2013, Augustus 20). PortalKBR.com. Retrieved November 1, 2013, from Benahi Angkutan Umum Segera agar Tahun Depan Jakarta Tak Macet Total: http://www.portalkbr.com/berita/perbincangan/2897988_4215.html
Manheim, L. (1979). Fundamental Transportation Systems Analysis (Vols. I, Basic Concept). The MIT Press.
Nur, M. Y., & Santika, D. D. (2011). Analisis Sentimen pada Dokumen Berbahasa Indonesia dengan Pendekatan Support Vector Machine. Konferensi Nasional Sistem dan Informatika. Bali.
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment Classification using Machine Learning Techniques. Proceedings of the ACL-02 conference on Empirical methods in natural language processing 10, (pp. 79-86).
Wicaksono, A. F., & Purwarianti, A. (2010, Agustus). HMM Based Part-of-Speech Tagger for Bahasa Indonesia. Proceedings of 4th International MALINDO (Malay – Indonesian Language).
Wulandini, F., & Nugroho, A. S. (2009). Text Classification Using Support Vector Machine for Webmining Based Spation Temporal
Analysis of the Spread of Tropical Diseases. International Conference on Rural Information and Communication Technology, (pp. 189-
.
Zhang, L., Ghosh, R., Dekhil, M., Hsu, M., & Liu, B. (2011, Juni). Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis. HPL-2011-89, 89.
Zhang, Weishi, Ding, G., Chen, L., & Li, C. (2010). Chinese Online Video Recomendation by Using Virtual Rating Predicted by Review Sentiment Classification. IEEE International Conference on Data Mining Workshop.
Downloads
Additional Files
Published
How to Cite
Issue
Section
License
Manuscript submitted to IJoICT has to be an original work of the author(s), contains no element of plagiarism, and has never been published or is not being considered for publication in other journals. Author(s) shall agree to assign all copyright of published article to IJoICT. Requests related to future re-use and re-publication of major or substantial parts of the article must be consulted with the editors of IJoICT.
.png)
.jpg)
