Entity Profiling to Identify Actor Involvement in Topics of Social Media Content

https://doi.org/10.22146/ijccs.59869

Puji Winar Cahyo(1*), Muhammad Habibi(2)

(1) Department of Informatics, FTTI UNJANI, Yogyakarta
(2) Department of Informatics, FTTI UNJANI, Yogyakarta
(*) Corresponding Author

Abstract


The efficiency of using social media affected modern society's nature and communication; they are more interested in talking through social media than meeting in the real world. The number of talks on social media content depends on the topic being discussed. The more topic interesting will impact the amount of data on social media will be. The data can be analyzed to get the influence of actors (account mentions) on the conversation. The power of an actor can be measured from how often the actor is mentioned in the conversation. This paper aims to conduct entity profiling on social media content to analyze an actor's influence on discussion. Furthermore, using sentiment analysis can determine the sentiment about an actor from a conversation topic. The Latent Dirichlet Allocation (LDA) method is used for analyzes topic modeling, while the Support Vector Machine (SVM) is used for sentiment analysis. This research can show that topics with positive sentiment are more likely to be involved in disaster management accounts, while topics with negative sentiment are more towards involvement in politicians, critics, and online news.

Keywords


Entity Profiling; Topic Modeling; Sentiment Analysis; LDA; SVM

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References

[1] D. Baum, M. Spann, J. Füller, and C. Thürridl, “Journal of Retailing and Consumer Services The impact of social media campaigns on the success of new product introductions,” J. Retail. Consum. Serv., no. xxxx, pp. 0–1, 2018.

[2] T. Macafee, B. Mclaughlin, and N. S. Rodriguez, “Winning on Social Media : Candidate Social-Mediated Communication and Voting During the 2016 US Presidential Election,” 2019.

[3] A. Gorrab, F. Kboubi, A. Jaffal, and L. Grand, “Twitter User Profiling Model Based on Temporal Analysis of Hashtags and Social Interactions,” in Natural Language Processing and Information Systems. NLDB 2017., 2017, vol. 1, pp. 124–130.

[4] P. W. Cahyo, “Klasterisasi Tipe Pembelajar Sebagai Parameter Evaluasi Kualitas Pendidikan Di Perguruan Tinggi,” Teknomatika, vol. 11, no. 1, pp. 49–55, 2018.

[5] S. PV and S. M. S. Bhanu, “UbCadet: detection of compromised accounts in twitter based on user behavioural profiling,” Multimed. Tools Appl., vol. 79, no. 27, pp. 19349–19385, 2020.

[6] M. Wiegmann, B. Stein, and B. Weimar, “Celebrity Profiling,” in The 57th Annual Meeting of the Association for Computational Linguistics, 2019, pp. 2611–2618.

[7] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. United Kingdom: Cambridge University Press, 2000.

[8] B. Liu, Web Data Mining : Exploring Hyperlinks, Contents, and Usage Data. Chicago: Springer, 2008.

[9] A. F. Hidayatullah and M. R. Maarif, “Penerapan Text Mining dalam Klasifikasi Judul Skripsi,” in Seminar Nasional Aplikasi Teknologi Informasi (SNATi) Agustus, 2016, pp. 1907–5022.

[10] M. Habibi and E. Winarko, “Klasifikasi Komentar Mahasiswa Menggunakan Kombinasi KNN berbasis Cosine Similarity dan Supervised Model,” no. x, pp. 1–11, 2017.

[11] C. Jacobi, W. Van Atteveldt, and K. Welbers, “Quantitative analysis of large amounts of journalistic texts using topic modelling,” Digit. Journal., vol. 4, no. 1, pp. 89–106, 2016.

[12] Y. Guo, S. J. Barnes, and Q. Jia, “Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation,” Tour. Manag., vol. 59, pp. 467–483, 2017.

[13] H. Jelodar, Y. Wang, C. Yuan, X. Feng, X. Jiang, Y. Li, and L. Zhao, “Stabilization of an Inverted Robot Arm Using Neuro-Controller,” Multimed. Tools Appl., vol. 78, pp. 183–198, 2018.

[14] A. F. Hidayatullah and M. R. Ma’Arif, “Road traffic topic modeling on Twitter using latent dirichlet allocation,” in 2017 International Conference on Sustainable Information Engineering and Technology (SIET), 2017, pp. 47–52.

[15] S. Moro, G. Pires, P. Rita, and P. Cortez, “A text mining and topic modelling perspective of ethnic marketing research,” J. Bus. Res., vol. 103, pp. 275–285, 2019.

[16] S. Mukherjee and P. Bhattacharyya, “Sentiment Analysis : A Literature Survey,” Indian Institute of Technology, Bombay, 2016.

[17] B. Liu, Sentiment Analysis and Opinion Mining, no. May. Morgan & Claypool Publishers, 2012.

[18] S. G. Kanakaraddi, A. K. Chikaraddi, K. C. Gull, and P. S. Hiremath, “Comparison Study of Sentiment Analysis of Tweets using Various Machine Learning Algorithms,” in Proceedings of the 5th International Conference on Inventive Computation Technologies, ICICT 2020, 2020, pp. 287–292.

[19] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, 2003.

[20] D. M. Blei and J. D. Lafferty, “Topic Models,” in Text Mining: Classification, Clustering, and Applications, 2009, p. 71.

[21] L. Hong and B. D. Davison, “Empirical study of topic modeling in Twitter,” in Proceedings of the First Workshop on Social Media Analytics - SOMA ’10, 2010, pp. 80–88.

[22] S. Varma, N. Sameer, and C. R. Chowdary, “ReLiC: entity profiling using random forest and trustworthiness of a source,” Sādhanā, vol. 44, no. 9, p. 200, 2019.

[23] P. W. Cahyo and M. Habibi, “Clustering followers of influencers accounts based on likes and comments on Instagram Platform,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 14, no. 2, pp. 199–208, 2020.

[24] M. Habibi and P. W. Cahyo, “Clustering User Characteristics Based on the influence of Hashtags on the Instagram Platform,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 13, no. 4, pp. 399–408, 2019.

[25] P. W. Cahyo and E. Winarko, “Model Monitoring Sebaran Penyakit Demam Berdarah di Indonesia Berdasarkan Analisis Pesan Twitter,” Universitas Gadjah Mada Yogyakarta, 2017.

[26] Y. Li, Z. Ding, X. Zhang, and B. Liu, “Confirmatory Analysis on Influencing Factors When Mention Users in Twitter,” in 2nd International Workshop on Web Data Mining and Applications (WDMA 2016), 2016, vol. 1, pp. 112–121.

[27] M. Habibi and P. W. Cahyo, “Journal Classification Based on Abstract Using Cosine Similarity and Support Vector Machine,” vol. 4, no. 3, pp. 48–55, 2020.

[28] A. A. Anees, H. Prakash Gupta, A. P. Dalvi, S. Gopinath, and B. R. Mohan, “Performance analysis of multiple classifiers using different term weighting schemes for sentiment analysis,” in 2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019, 2019, pp. 637–641.

[29] K. Stevens, P. Kegelmeyer, D. Andrzejewski, and D. Buttler, “Exploring topic coherence over many models and many topics,” in Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP), 2012, no. July, pp. 952–961.

[30] F. WH, “Pedoman Ibadah Ramadan dan Lebaran di Masa Pandemi Covid-19 | Indonesia.go.id,” Indonesia.Go.Id, Apr-2020. .

[31] A. W. Bartik, M. Bertrand, Z. Cullen, E. L. Glaeser, M. Luca, and C. Stanton, “The impact of COVID-19 on small business outcomes and expectations.,” Proc. Natl. Acad. Sci. U. S. A., vol. 117, no. 30, pp. 17656–17666, Jul. 2020.

[32] N. Wira Sakti, “Perekonomian Indonesia Pasca-Pandemi Covid-19 Halaman all - Kompas.com,” Kompas.com, May-2020. .

[33] N. Kapasia, P. Paul, A. Roy, J. Saha, A. Zaveri, R. Mallick, B. Barman, P. Das, and P. Chouhan, “Impact of lockdown on learning status of undergraduate and postgraduate students during COVID-19 pandemic in West Bengal, India,” Child. Youth Serv. Rev., vol. 116, p. 105194, Sep. 2020.

[34] R. Jose, M. Narendran, A. Bindu, N. Beevi, M. L, and P. V. Benny, “Public perception and preparedness for the pandemic COVID 19: A Health Belief Model approach,” Clin. Epidemiol. Glob. Heal., Jun. 2020.

[35] T. K. Yunianto, “Survei: 63% Responden Puas Atas Kerja Gugus Tugas Tangani Covid-19 - Nasional Katadata.co.id,” https://katadata.co.id/, Jun-2020. .

[36] C. Indonesia, “Panduan Lengkap Pembukaan Rumah Ibadah di Masa Pandemi,” https://www.cnnindonesia.com/, May-2020.

[37] P. Yasmin, “Panduan Ibadah di Masjid Selama Pandemi dari MUI DKI Jakarta,” https://news.detik.com/, Jun-2020.

[38] R. Gunadha, “Budayawan Sudjiwo Tedjo Beri Ucapan Terima Kasih pada Corona, Mengapa?,” Sep-2020.



DOI: https://doi.org/10.22146/ijccs.59869

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