Exploring Sentiment Analysis in Kannada Language: A Comprehensive Study on COVID-19 Data using Machine Learning and Ensemble Algorithms
Keywords:
Sentiment Analysis, Machine Learning, Ensemble, Scikit Learn, COVID-19Abstract
COVID-19 changed several lives in the past few years. The world came to a halt and the situation forced everyone to stay home and this brought a huge shift in people’s sentiments for several reasons. Various countries were severely affected due to the lockdown including various regions of India. India is a multi-lingual country which consists of various regional languages. In this paper, Sentiment Analysis is performed on COVID-19 data which is in Kannada using three Machine Learning algorithms along with three Ensemble algorithms. Employing a diverse set of models, the study aims to enhance prediction accuracy, feature selection, and overall understanding of the pandemic's dynamics. By leveraging advanced techniques, the research contributes valuable insights for optimizing decision-making processes in healthcare and public policy. The findings demonstrate the potential of integrating machine learning into epidemiological studies for a more nuanced and effective response to global health crises.
Downloads
References
Anil Kumar K.M, N. Rajasimha, “Analysis of users’ Sentiments from Kannada Web Documents”, Procedia Computer Science, pp 247-256, 2015.
Shankar R, Suma Swamy, “Corpora Based Classification to Perform Sentiment Analysis in Kannada Language”, The Design Engineering, pp 647-656, 2021.
Shankar R, Suma Swamy, “Sentiment Analysis of Kannada Political Tweets using Support Vector Machines”, International Journal of Recent Technology and Engineering, pp 5186-5191, 2020.
Adeep Hande , Ruba Priyadharshini , Bharathi Raja Chakravarthi,” KanCMD: Kannada CodeMixed Dataset for Sentiment Analysis and Offensive Language Detection”, Proceedings of the Third Workshop on Computational Modeling of People’s Opinions, PersonaLity, and Emotions in Social media, pp 54–63, 2020.
Adeep Hande et al, “Hope Speech detection in under-resourced Kannada language”, Computation and Language, pp 2108-2119, 2021.
Mahesh B. Shelke , Sachin N. Deshmukh, “Recent Advances in Sentiment Analysis of Indian Languages”, International Journal of Future Generation Communication and Networking, pp 1656-1675, 2020.
Ramesh Chundi; Vishwanath R. Hulipalled; J.B Simha, “SAEKCS: Sentiment Analysis for English – Kannada Code SwitchText Using Deep Learning Techniques”, International Conference on Smart Technologies in Computing, Electrical and Electronics, pp 327-331, 2020.
Prashanth, Vidya Bhat, “A comparison of Bayesian and HMM based approaches in machine learning for emotion detection in native Kannada speaker”, IEEE International WIE Conference on Electrical and Computer Engineering pp 1-6, 2019.
Anil Kumar KM, Asmita Poojari, Mohana kumari M, “Pattern based approach for mining users opinion from Kannada web documents”, Discovery Journal, pp 138-143, 2015.
Venkatachalam Kandasamy et al, “Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme”, Sensors (Basel), pp 15-21, 2021.
Geethashree A and Dr. D.J Ravi, “Acoustic and Spectral Analysis of Kannada Emotional Speech”, Third International Conference on Current Trends in Engineering Science and Technology, pp 42-49, 2017.
Prashanth, Vidya Bhat, “Comparison of Hidden Markov Model and Artificial Neural Network Based Machine Learning Techniques Using DDMFCC Vectors for Emotion Recognition in Kannada”, IEEE International WIE Conference on Electrical and Computer Engineering, pp 1-6, 2019.
Abhinav Reddy Appidi et al, “Creation of Corpus and analysis in Code-Mixed Kannada-English Twitter data for Emotion Prediction”, Proceedings of the 28th International Conference on Computational Linguistics, pp 6703—6709, 2020.
Muvazima Mansoor et al, “Global Sentiment Analysis Of COVID-19 Tweets Over Time”, Computation and Language, pp 1423-1430, 2020.
Anna Kruspe et al, ”Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic”, Social and Information Networks, pp 2008-2015, 2020.
Pradeep Kumar, “A Deep Ensemble Network for Sentiment Analysis in Bi-Lingual Low-Resource Languages”, ACM Transactions on Asian and Low-Resource Language Information Processing, pp 2375-4699, 2023.
Ramanathan, Bing Liu and Alok Choudhary, “Sentiment Analysis of Conditional Sentences”, Proceedings of Conference on Empirical Methods in Natural Language Processing, pp 213-221, 2009.
Soumya et al, “Sentiment analysis of malayalam tweets using machine learning techniques”, ICT Express, pp 300-305, 2009.
Rajesh Bose, Raktim Kumar Dey, Sandip Roy and Debabrata Sarddar, “Analyzing Political Sentiment Using Twitter Data”, Smart Innovation, Systems and Technologies, pp 427–436, 2018.
Marjan Van de Kauter et al, “Fine-grained analysis of explicit and implicit sentiment in financial news articles”, Expert Systems with Applications, pp 4999-5010, 2015.
David Vilares, Miguel A. Alonso and Carlos G ́omez-Rodr ́ıguez, “Sentiment Analysis on Monolingual, Multilingual and Code-Switching Twitter Corpora”, Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp 2-8, 2015.
Alexandra Balahur, Marco Turchi, “Computer Speech & Language”, ICT Express, pp 56-75, 2014.
Julian Brooke et al, “Cross-Linguistic Sentiment Analysis: From English to Spanish”, International Conference RANLP, pp 50-54, 2009.
Erik et al, “Knowledge-Based Approaches to Concept-Level Sentiment Analysis”, Intelligent Systems, IEEE, pp 12-14, 2013.
Arouna et al, “Sentiment Analysis of Code-Mixed Bambara-French Social Media Text Using Deep Learning Techniques”, Wuhan University Journal of Natural Sciences, pp 237–243, 2018.
Anqi Cui et al, “Emotion Tokens: Bridging the Gap among Multilingual Twitter Sentiment Analysis”, Asia Information Retrieval Symposium, pp 238-249, 2011.
Alex et al, “Language-independent Bayesian sentiment mining of Twitter.” The 5th SNA-KDD Workshop, pp 11-19, 2011.
Preslav Nakov et al, “SemEval-2013 Task 2: Sentiment Analysis in Twitter”, Second Joint Conference on Lexical and Computational Semantics, pp 312–320, 2013.
Sachin Kumar et al, “Identifying Sentiment of Malayalam Tweets Using Deep Learning”, Lecture Notes on Data Engineering and Communications Technologies, pp 391-408, 2018.
Julio et al, “TASS 2014-The Challenge of Aspect-based Sentiment Analysis”, Procesamiento de Lenguaje Natural, 61-68, 2015.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
