South India Journal of Social Sciences is now an official member of Crossref JGate sgd
Examining the emotional tone in politically polarized Speeches in India: An In-Depth analysis of two contrasting perspectives
ARTICLE PDF FILE

Keywords

Natural Language Processing
Semantic Latent Analysis
Singular Value Decomposition
Latent Dirchlet Allocation

How to Cite

DwijendraNath Dwivedi, Aravind Kumar Pandey, & Aditya Dhar Dwivedi. (2023). Examining the emotional tone in politically polarized Speeches in India: An In-Depth analysis of two contrasting perspectives. SOUTH INDIA JOURNAL OF SOCIAL SCIENCES, 21(2), 125-136. https://journal.sijss.com/index.php/home/article/view/65

Abstract

Sentiment analysis is an area of natural language processing (NLP) which uses machine learning algorithms to interpret textual data. Recently, sentiment analysis has become an increasingly popular trend within social sciences studies led by computer scientists. NLP techniques have recently been applied to several political science topics, such as topic modeling and supervised machine learning. These techniques help political scientists recognize underlying ideology or misinformation present in texts as well as detect changes over time in political messages.  This study examines the application of topic modeling on political speeches to understand the recurring themes present in them. Recent speeches for last one year for the prime minister of India and key opposition party leader has been selected for the same. It delves into the coherence of the topics generated through the topic modeling method and the influence of text size on the coherence of political speech themes. The analysis focuses on determining the presence of positive or negative sentiments in public speeches and the utilization of Latent Dirichlet Allocation (LDA) and Coherence Measures as techniques for modeling and analyzing public speeches. The LDA algorithm operates by analyzing a speech and determining the number of words it includes, then identifying the topics. Paper, find one views to be extremely positive and other one with negative sentiments. Also, paper shares some of the key topics for the same

ARTICLE PDF FILE

References

Alghamdi, R., & Alfalqi, K. (2015). "A Survey of Topic Modeling in Text Mining". International Journal of Advanced Computer Science and Applications, 6(1),147-153.

Al-Obeidat et. al. (2018). "Opinions Sandbox: Turning Emotions on Topics into Actionable Analytics". Lecture Notes of the Institute for Computer Sciences, Social- Informatics and Telecommunications Engineering, LNICST, 206, 110-119. https://doi.org/10.1007/978-3-319-67837-5_11"

Asmussen, C. B., & Møller, C. (2019). "Smart literature review: a practical topic modelling approach to exploratory literature review". Journal of Big Data,6(1).

Benedetto, F., &Tedeschi, A. (2016). "Big data sentiment analysis for brand monitoring in social media streams of cloud computing". In Studies in Computational Intelligence (Vol.639).

Conover M, Gonçalves B, Ratkiewicz J, Flammini A, Menczer F. (2011a) "Predicting the political alignment of Twitter users." In Proceedings of SocialCom/PASSAT Conference, pp.192-199

Deerwester,S.,Dumaiset.al(1990)."Indexing by latent semantic analysis". Journal of the American Society for Information Science, 41(6), 391-407"

Dwivedi D.N., Anand A. (2022) "A Comparative Study of Key Themes of Scientific Research Post COVID-19 in the United Arab Emirates and WHO Using Text Mining Approach". In: Tiwari S., Trivedi M.C., Kolhe M.L., Mishra K., Singh B.K. (eds)

Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 318. Springer, Singapore. https://doi.org/10.1007/978-981-16-5689-7_30''

Dwivedi, D. N., Mahanty, G., & Vemareddy, A. (2022). How Responsible Is AI?: Identification of Key Public Concerns Using Sentiment Analysis and Topic Modeling. International Journal of Information Retrieval Research (IJIRR), 12(1), 1-14. http://doi.org/10.4018/IJIRR.298646

Dwivedi, D., Vemareddy, A. (2023). Sentiment Analytics for Crypto Pre and Post Covid: Topic Modeling. In: Molla, A.R., Sharma, G., Kumar, P., Rawat, S. (eds) Distributed Computing and Intelligent Technology. ICDCIT 2023. Lecture Notes in Computer Science, vol 13776. Springer, Cham. https://doi.org/10.1007/978-3-031-24848-1_21

Dwivedi, D.N., Mahanty, G., Vemareddy, A. (2023). Sentiment Analysis and Topic Modeling for Identifying Key Public Concerns of Water Quality/Issues. In: Harun, S., Othman, I.K., Jamal, M.H. (eds) Proceedings of the 5th International Conference on Water Resources (ICWR) - Volume 1. Lecture Notes in Civil Engineering, vol 293. Springer, Singapore. https://doi.org/10.1007/978-981-19-5947-9_28

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2023 SOUTH INDIA JOURNAL OF SOCIAL SCIENCES