Pemodelan Topik dan Analisis Sentimen pada 'Voices of History: 50 Iconic Speeches' Menggunakan Pendekatan Natural Language Processing
Abstract
This study examines various iconic speeches throughout history using Natural Language Processing (NLP) approaches. The selected speeches address a wide range of historical and social issues, ranging from the struggle for civil rights upto the calls for peace.. This analysis is carried out to reveal the sentiments, themes and emotions expressed in each speech, as well as to understand how orators use language to influence and inspire their audience. This study identifies common patterns and themes in these speeches by utilizing text vectorization methods, topic extraction using Latent Dirichlet Allocation (LDA), as well as sentiment and emotion analysis. The findings of this analysis show that the most influential speeches mostly include strong themes such as social justice, freedom, and unity. Furthermore, good thoughts and powerful emotions such as optimism and courage are frequently featured in the most motivating speeches. This research provides valuable insight for historians, researchers, and oratory enthusiasts into how iconic speeches have shaped public opinion and influenced social change.
Keywords
Full Text:
PDF (Bahasa Indonesia)References
Bhuvaneshwari Kondeti, S.A. Jyothiranis, Haragopal Venkata Vajjha. (2023). Topic Modelling from Repository of Political Speeches. International Journal For Innovative Research In Multi-disciplinary Field, ISSN: 2455-0620, Volume 9, Issue 9.
Biyani, P., Bhatia, S., Caragea, C., & Mitra, P. (2014). Using Non-lexical Features for Identifying Factual and Opinionative Threads in Online Forums. Knowledge-Based Systems, 91, 49-56.
Blei, D. M., & Jordan, M. I. (2003). Latent Dirichlet Allocation for Topic Modeling. Journal of Machine Learning Research, 3, 993-1022.
Buechel, S., & Hahn, U. (2016). Emotion Analysis as a Regression Problem: Dimensional Models and Their Impli-cations on Emotion Representation and Metrical Evaluation. Proceedings of the 22nd Annual Meeting of the Association for Computational Lingu-istics (ACL), 111-121.
Cochrane, C., Rheault, L., Godbout, J. F., Whyte, T., Wong, M.W. C., & Borwein, S. (2021). The Automatic Analysis of Emotion in Political Speech Based on Transcripts. Political Communication, 39(1), 98–121. https://doi.org/10.1080/
2021.1952497.
Enjang Pera Irawan (2022), Political Campaign Strategy of Young PKS Politicians In Seeking Constituent Support, Jurnal Ilmiah Manajemen Informasi dan Komunikasi, Vol 6, No. 1, ISSN : 2614-1272.
Indah Suryawati (2021), Political Entertain-ment Of Central Parlemen Celebrity In Online Media Construction, Jurnal Ilmiah Manajemen Informasi dan Komunikasi,Vol 5, No. 1, ISSN : 2614-1272.
Iyyer, M., Enns, P., Boyd-Graber, J., & Resnik, P. (2014). Political Ideology Detection Using Recursive Neural Networks. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 1113-1122.
Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent Dirichlet Allocation (LDA) and Topic Modeling: Models, Applications, a Survey. Multimedia Tools and Applications, 78(11), 15169-15211.
Jianqiang, Z., Xiaolin, G., & Xuejun Z. (2018). Deep Convolution Neural Networks for Twitter Sentiment Analysis. Applied Sciences, 6(3), 52.
Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing (3rd ed.). Pearson.
Mac Aditiawarman, Oki Agustriawan (2020), An Analysis of Speech The Presidential Candidates Debate of United States in 2020. Jurnal Ilmiah Langue and Parole, Volume 6, Nomor 1, E-ISSN : 2581-1819.
Munezero, M., Montero, C. S., Sutinen, E., & Pajunen, J. (2014). Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text. IEEE Transactions on Affective Computing, 5(2), 101-111.
Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The Development and Psychometric Properties of LIWC 2015. Austin, TX: University of Texas at Austin.
Tindale, C. W. (2019). The Art of Persuasion in Aristotle's Rhetoric. Routledge.
William L Benoit (2015). Political Election Debates: Informing Voters about Policy and Character. Lexington Books, University of Alabama Birmingham, ISBN-13978-1498515610.
Zhang, L., Wang, S., & Liu, B. (2018). Deep Learning for Sentiment Analysis: A Survey. Wiley Interdisciplinary Re-views: Data Mining and Knowledge Discovery, 8(4), e1253.
DOI: https://doi.org/10.56873/jimik.v8i1.330
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Juniana Husna, Margareth Dyah Anggraini Widirahayu
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright of Jurnal Ilmiah Manajemen Informasi dan Komunikasi
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
ISSN Print: 2614-1272. ISSN Online: 2720-9857
Penerbit: Sekolah Tinggi Multi Media "MMTC" Yogyakarta
Alamat:
Jln. Magelang Km. 6 Yogyakarta
Telepon: (0274) 561531, 562513
Fax: (0274) 623537
Email: jurnal.mik@mmtc.ac.id