SENTIMENT ANALYSIS OF PERFORMANCE EFFECTIVENESS OF MALIOBORO PEDESTRIAN USING SENTISTRENGTH METHOD ON TWITTER
(1) Department of Informatics, Institut Sains & Teknologi AKPRIND Yogyakarta
(2) Department of Informatics, Institut Sains & Teknologi AKPRIND Yogyakarta
Corresponding Author
Abstract
Sentiment analysis is a study to analyze public opinions, sentiments, evaluations, attitudes, and emotions towards services, products, public issues, organizations, general topics, etc. Sentiment analysis is a computational research of various opinions and emotionsexpressed textually and opinions in the form of text can be obtained through social media such as Twitter. The Malioboro area as one of the famous tourist destinations in Yogyakarta has pedestrian facilities for visitors. In the area, there are many pedestrian facilities including pedestrian paths, sidewalks, zebra crossings and parking. This study aims to measure the effectiveness of the use of the pedestrian area in Malioboro based on opinions on Twitter. This study uses the Sentistrength method. The results shows that from 3,572 Tweet data from 2016 to 2020, the results of Positive sentiment are 55.81%, the results of Neutral sentiment are 36.18% and the results of negative sentiment are 8.01%.
Keywords
References
APJII, “Penetrasi & Profil Perilaku Pengguna Internet Indonesia Tahun 2018”, Apjii, bl 51, 2019.
D. H. Wahid en A. SN, “Peringkasan Sentimen Esktraktif di Twitter Menggunakan Hybrid TF-IDF dan Cosine Similarity”, IJCCS (Indonesian J. Comput. Cybern. Syst., vol 10, no 2, bl 207, 2016.
L. Yue, W. Chen, X. Li, W. Zuo, en M. Yin, “A survey of sentiment analysis in social media”, Knowl. Inf. Syst., 2019.
B. Liu, Sentiment analysis: Mining opinions, sentiments, and emotions. 2015.
A. R. Alaei, S. Becken, en B. Stantic, “Sentiment Analysis in Tourism: Capitalizing on Big Data”, Journal of Travel Research. 2019.
Peraturan Menteri Pekerjaan Umum Nomor : 03/PRT/M/2014, “Pedoman Perencanaan, Penyediaan, dan Pemanfaatan Prasarana dan Sarana Jaringan Pejalan Kaki di Kawasan Perkotaan”, Menteri Pekerj. Umum Republik Indones., vol 2013, bl 8, 2014.
F. P. Nugroho en Y. D. Pambudi, “Analisa Brand Reputation Wisata Daerah Menggunakan Sentimen Data Twitter (Studi Kasus: Museum Sangiran Kabupaten Sragen)”, J. Inf., vol 6, no 1, bll 40–45, 2020.
E. Indrayuni, “Analisa Sentimen Review Hotel Menggunakan Algoritma Support Vector Machine Berbasis Particle Swarm Optimization”, J. Evolusi Vol. 4 Nomor 2 - 2016, 2016.
M. R. Islam en M. F. Zibran, “SentiStrength-SE: Exploiting domain specificity for improved sentiment analysis in software engineering text”, J. Syst. Softw., 2018.
O. A. M. Ghaleb en A. S. Vijendran, “An enhancement of the public sentiment analysis on social networking by improving sentiment analysis tools”, Int. J. Intell. Eng. Syst., 2018.
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DOI: 10.56327/jurnaltam.v12i1.1044
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