Clustering Content Types and User Motivation Using DBSCAN on Twitter

Made Mita Wikantari; Yuliant Sibaroni; Aditya Firman Ihsan

doi:10.47065/josyc.v4i4.3750

Made Mita Wikantari * Telkom University, Bandung, Indonesia
Yuliant Sibaroni Telkom University, Bandung, Indonesia
Aditya Firman Ihsan Telkom University, Bandung, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/josyc.v4i4.3750

Keywords: Twitter; Clustering; DBSCAN; TF-IDF Vectorizer; Silhouette Score

Abstract

We are currently in an era full of information and communication technology. One of the communication media used is Twitter. Twitter is a microblogging service that is used by its users to express their thoughts on a topic called a tweet. Tweets that are posted can be either positive tweets or negative tweets. One of the topics that is currently being discussed by Twitter users is Anies Baswedan as a 2024 Indonesian Presidential Candidate. Many people have tweeted this but it is not known how many users support or reject Anies Baswedan to run as a 2024 Indonesian presidential candidate. To assist the analysis, use the method clustering namely algorithm (Density-Based Spatial Clustering of Application with Noise). DBSCAN has the advantage of being able to detect data that is not included in a cluster and will be considered noise. This can improve the accuracy of the grouping because the data in the cluster will be cleaner. The TF-IDF Vectorizer is used to make it easier for programs to manage data because it can turn sentences into vectors that can be processed by the algorithm. To determine the evaluation of the program, the silhouette score method will be used. The results of calculating the silhouette score show a value of 0.29 with the formation of 3 clusters. Then an analysis is carried out based on the top words from each cluster and it can be identified that cluster 0 has a positive category supporting Anies Baswedan to run for the 2024 Presidential Candidate and cluster 1 has a negative category that does not support Anies Baswedan not advancing for the 2024 Presidential Candidate.

Downloads

Download data is not yet available.

References

G. K. Jha and T. Ramakrishnudu, “User Behavior Pattern and Deeper Intention Analysis in Online Social Media,” in 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), 2019, pp. 1–5.

A. C. Sari, R. Hartina, R. Awalia, H. Irianti, and N. Ainun, “Komunikasi dan media sosial,” J. Messenger, vol. 3, no. 2, p. 69, 2018.

N. Hermawan, “Representasi Anies dan Ganjar pada Bursa Calon Presiden Indonesia 2024 dalam Berita Online Okezone. com,” Syntax Lit. J. Ilm. Indones., vol. 6, no. 1, pp. 24–32, 2021.

R. N. G. Indah et al., “DBSCAN algorithm: twitter text clustering of trend topic pilkada pekanbaru,” in Journal of physics: conference series, 2019, vol. 1363, no. 1, p. 12001.

R. Dolan, J. Conduit, C. Frethey-Bentham, J. Fahy, and S. Goodman, “Social media engagement behavior: A framework for engaging customers through social media content,” Eur. J. Mark., vol. 53, no. 10, pp. 2213–2243, 2019.

Y.-T. Huang and S.-F. Su, “Motives for Instagram use and topics of interest among young adults,” Futur. internet, vol. 10, no. 8, p. 77, 2018.

A. Herdiani and I. Asror, “Klasterisasi Tweet Terkait Dengan Pemilihan Presiden 2019 Menggunakan Ontology-based Concept Weighting dan DBSCAN,” eProceedings Eng., vol. 6, no. 2, 2019.

I. Kurniawan and A. Susanto, “Implementasi Metode K-Means dan NaÃ ve Bayes Classifier untuk Analisis Sentimen Pemilihan Presiden (Pilpres) 2019,” J. Eksplora Inform., vol. 9, no. 1, pp. 1–10, 2019.

E. B. Setiawan, D. H. Widyantoro, and K. Surendro, “Feature expansion for sentiment analysis in twitter,” in 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2018, pp. 509–513.

T. Mustaqim, K. Umam, and M. A. Muslim, “Twitter text mining for sentiment analysis on government’s response to forest fires with vader lexicon polarity detection and k-nearest neighbor algorithm,” in Journal of Physics: Conference Series, 2020, vol. 1567, no. 3, p. 32024.

A. Hassani, A. Iranmanesh, and N. Mansouri, “Text mining using nonnegative matrix factorization and latent semantic analysis,” Neural Comput. Appl., vol. 33, pp. 13745–13766, 2021.

A. M. Pravina, I. Cholisoddin, and P. P. Adikara, “Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine (SVM),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 2789–2797, 2019.

J. Kaur and P. K. Buttar, “A systematic review on stopword removal algorithms,” Int. J. Futur. Revolut. Comput. Sci. Commun. Eng., vol. 4, no. 4, pp. 207–210, 2018.

M. Haroon, “Comparative analysis of stemming algorithms for web text mining,” Int. J. Mod. Educ. Comput. Sci., vol. 10, no. 9, pp. 20–25, 2018.

M. Z. Fauzi and A. Abdullah, “Clustering of Public Opinion on Natural Disasters in Indonesia Using DBSCAN and K-Medoids Algorithms,” in Journal of Physics: Conference Series, 2021, vol. 1783, no. 1, p. 12016.

R. Novia, S. S. Prasetyowati, and Y. Sibaroni, “Identify User Behavior Based on The Type of Tweet on Twitter Platform Using Gaussian Mixture Model Clustering,” J. Comput. Syst. Informatics, vol. 3, no. 4, pp. 502–506, 2022.

S. F. Galán, “Comparative evaluation of region query strategies for DBSCAN clustering,” Inf. Sci. (Ny)., vol. 502, pp. 76–90, 2019.

D. Deng, “DBSCAN clustering algorithm based on density,” in 2020 7th international forum on electrical engineering and automation (IFEEA), 2020, pp. 949–953.

W. Gunawan, “Implementasi Algoritma DBScan dalam Pemngambilan Data Menggunakan Scatterplot,” Techno Xplore J. Ilmu Komput. dan Teknol. Inf., vol. 6, no. 2, pp. 91–98, 2021.

D. W. Laraswati and A. Fauzan, “Implementasi Metode Runtun Waktu dalam Pemodelan Total Harga Alat Kedokteran dan Kesehatan,” Jambura J. Probab. Stat., vol. 4, no. 1, pp. 30–38, 2023.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Clustering Content Types and User Motivation Using DBSCAN on Twitter