IJBTT

Aspect Based Polarity Extraction in Tamil Tweets using Tree-Based Recursive Partitioning Techniques

Aspect Based Polarity Extraction in Tamil Tweets using Tree-Based Recursive Partitioning Techniques
	© 2022 by IJETT Journal
	Volume-70 Issue-12
	Year of Publication : 2022
	Author : S. Rajeswari, S. Gokila, K. Thinakaran, R.Surendiran
	DOI : 10.14445/22315381/IJETT-V70I12P240

How to Cite?

S. Rajeswari, S. Gokila, K. Thinakaran, R.Surendiran, "Aspect Based Polarity Extraction in Tamil Tweets using Tree-Based Recursive Partitioning Techniques," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 421-430, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I12P240

Abstract
The overall outcome of the emotional statement about one particular discussion falls into two positive or negative that can be identified by the word/words and their synonymous that are closely connected with the theme of the topic. This work aims to identify the impacting word of the motion and analyse the performance of the Tree-based Machine Learning (ML) classifiers to classify the Tamil Tweets into two polarities (positive or negative). All the models are separately trained and tested with both Non-Weighted Vector and Weighted Vectors and analysed to freeze the accuracy. The prelabelled 1015 Tamil tweets are pre-processed to remove the noises to form a word dictionary. The words in the dictionary are tagged with weight to indicate the impact. The structured corpus with various lengths of statements is experimented with using a Decision tree, XGBoost and Random Forest classifiers with varying parameters. The comparative study report shows that Random Forest performs well by showing 78.81% of accuracy with Weighted Vector, which is better compared with Decision Tree and XGBoost classifiers.

Keywords
Decision tree, XGBoost, Random forest, Natural Language Processing, Classification.

References
[1] N. Rajkumar et al., "An Efficient Feature Extraction with Subset Selection Model Using Machine Learning Techniques for Tamil Documents Classification," International Journal of Advanced Research in Engineering and Technology, vol. 11, no. 11, pp. 66-81, 2020.
[2] Shriya Se et al., "Predicting the Sentimental Reviews in Tamil Movie using Machine Learning Algorithms," Indian Journal of Science and Technology, vol. 9, no. 45, pp. 1-5, 2016. Crossref, http://doi.org/10.17485/ijst/2016/v9i45/106482
[3] Diksha Khurana et al., "Natural Language Processing: State of the Art, Current Trends and Challenges," Multimedia Tools and Applications, 2022. Crossref, https://doi.org/10.1007/s11042-022-13428-4
[4] Xueying Zhang, and Xianghan Zheng, "Comparison of Text Sentiment Analysis based on Machine Learning," 15th International Symposium on Parallel and Distributed Computing, pp. 230-233, 2016. Crossref, https://doi.org/10.1109/ISPDC.2016.39
[5] Anuj Gupta et al., "Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems," United States, O'Reilly Media, 2020.
[6] Samheeta Gourammolla, and S Gokila, "HCB Machine Learning Approach for Movie Recommendation System," 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, pp. 1186-1190, 2022. Crossref, https://doi.org/10.1109/ICICCS53718.2022.9788163
[7] Wei Li et al., "Bieru: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis," Neurocomputing, vol. 467, pp. 73-82, 2022. Crossref, https://doi.org/10.1016/j.neucom.2021.09.057
[8] Joni Salminen et al., "Creating and Detecting Fake Reviews of Online Products," Journal of Retailing and Consumer Services, vol. 64, p. 102771, 2022. Crossref, https://doi.org/10.1016/j.jretconser.2021.102771
[9] K. Kavitha, and Suneetha Chittineni, "Efficient Sentimental Analysis using Hybrid Deep Transfer Learning Neural Network," International Journal of Engineering Trends and Technology, vol. 70, no. 10, pp. 155-165, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I10P216
[10] Veny Amilia Fitri, Rachmadita Andreswari, and Muhammad Azani Hasibuan, "Sentiment Analysis of Social media Twitter with Case Anti LGBT Campaign in Indonesia using Naïve Bayes, Decision Tree and Random Forest Algorithm," Procedia Computer Science, vol. 161, pp. 765-772, 2019. Crossref, https://doi.org/10.1016/j.procs.2019.11.181
[11] P. Karthika, R. Murugeswari, and R. Manoranjithem, "Sentiment Analysis of Social Media Network Using Random Forest Algorithm," 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), pp. 1-5, 2019. Crossref, https://doi.org/10.1109/INCOS45849.2019.8951367
[12] Yassine Al Amrani, Mohamed Lazaar, and Kamal Eddine El Kadiri, "Random Forest and Support Vector Machine based Hybrid Approach to Sentiment Analysis," Procedia Computer Science, vol. 127, pp. 511-520, 2018. Crossref, https://doi.org/10.1016/j.procs.2018.01.150
[13] Mohammad Aufar, Rachmadita Andreswari, and Dita Pramesti, "Sentiment Analysis on Youtube Social Media Using Decision Tree and Random Forest Algorithm: A Case Study," 2020 International Conference on Data Science and Its Applications (ICoDSA), pp. 1-7, 2020. Crossref, https://doi.org/10.1109/ICoDSA50139.2020.9213078
[14] Yashaswin Hegde, and S.K.Padma, "Sentiment Analysis using Random Forest Ensemble for Mobile Product Reviews in Kannada," IEEE 7th International Advance Computing Conference, pp. 777-782, 2017. Crossref, https://doi.org/10.1109/IACC.2017.0160
[15] Jeevanandam Jotheeswaran, and S. Koteeswaran, "Feature Selection using Random Fores Method for Sentiment Analysis," Indian Journal of Science and Technology, vol. 9, no. 3, pp. 1- 6, 2016. Crossref, https://doi.org/10.17485/ijst/2016/v9i3/75971
[16] Gayatri Khanvilkar, and Deepali Vora, "Sentiment Analysis for Product Recommendation Using Random Forest," International Journal of Engineering and Technology (UAE), vol. 7, no. 3.3, pp. 87-89, 2018. Crossref, https://doi.org/10.14419/ijet.v7i3.3.14492
[17] Sajeetha Thavareesan, and Sinnathamby Mahesan, "Sentiment Lexicon Expansion using Word2Vec and Fast Text for Sentiment Prediction in Tamil Texts," 2020 Moratuwa Engineering Research Conference (MERCon), IEEE, pp. 272-276. Crossref, https://doi.org/10.1109/MERCon50084.2020.9185369
[18] E. Sivasankar, K. Krishnakumari, and P. Balasubramanian, "An Enhanced Sentiment Dictionary for Domain Adaptation with Multi-Domain Dataset in Tamil Language (ESD-DA)," Soft Computing, vol. 25, no. 2, pp. 3697-3711, 2021. Crossref, https://doi.org/10.1007/s00500-020-05400-x
[19] Jerry Wood, "COVID-19: The Pandemic's Impact on the Dissemination of Data in Virtual Teams using Computer-Mediated Communication Technology," International Journal of Computer Trends and Technology, vol. 68, no. 12, pp. 26-30, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I12P106
[20] Thevatheepan Priyadharshan, and Sagara Sumathipala, “Text Summarization for Tamil Online Sports News Using NLP,” 2018 3rd International Conference on Information Technology Research (ICITR), IEEE, pp. 1-5, 2018. Crossref, https://doi.org/10.1109/ICITR.2018.8736154
[21] Bharathi Raja Chakravarthi et al., "Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text," Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL), pp. 202-210, 2020.
[22] Balaji Karumanchi, "An Unsupervised Clustering Approach for Twitter Sentimental Analysis: A Case Study for George Floyd Incident," International Journal of Computer Trends and Technology, vol. 68, no. 6, pp. 46-50, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I6P10
[23] Thorvardur Jon Love, Tianxi Cai, and Elizabeth W.Karlson, "Validation of Psoriatic Arthritis Diagnoses in Electronic Medical Records Using Natural Language Processing," Seminars in Arthritis and Rheumatism, vol. 40, no. 5, pp. 413-420, 2011. Crossref, https://doi.org/10.1016/j.semarthrit.2010.05.002
[24] Amit Gupte et al., "Comparative Study of Classification Algorithms used in Sentiment Analysis," International Journal of Computer Science and Information Technologies, vol. 5, no. 5, pp. 6261-6264, 2014.
[25] Xiaohui Liang et al., "Evaluating Voice-Assistant Commands for Dementia Detection," Computer Speech & Language, vol. 72, p. 101297, 2022. Crossref, https://doi.org/10.1016/j.csl.2021.101297
[26] Md. Sirajul Huque, and V. Kiran Kumar, "A Study on Sentiment Analysis of Movie Reviews using ML Algorithms," International Journal of Computer Trends and Technology, vol. 70, no. 9, pp. 33-37, 2022. Crossref, https://doi.org/10.14445/22312803/IJCTT-V70I9P104
[27] Abhijeet Mankar, and Sudhakar Bhoite, "Review of Literature on Recursive Partitioning and its Applications in Various Area," Proceedings of the International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 2022. Crossref, https://doi.org/10.4108/eai.16-4-2022.2318071
[28] Roza Hikmat Hama Aziz, and Nazife Dimililer, "SentiXGboost: Enhanced Sentiment Analysis in Social Media Posts with Ensemble Xgboost Classifier," Journal of the Chinese Institute of Engineers, vol. 44, no. 6, pp. 562–572, 2021. Crossref, https://doi.org/10.1080/02533839.2021.1933598