Developing sentiment lexicon for Marathi : A comprehensive survey and analysis
*Pallavi V. KulkarniCorresponding authorpallavikulkarni.phdcomp@mmcoe.edu.inAffiliation 1Department of Computer EngineeringMarathwada Mitra Mandal’s College of EngineeringPune, Maharashtra, IndiaAffiliation 2Karve NagarSavitribai Phule Pune UniversityPune, Maharashtra, IndiaView full profile → , Kalpana S. Thakrekalpanathakre@mmcoe.edu.inAffiliation 1Department of Computer EngineeringMarathwada Mitra Mandal’s College of EngineeringPune, Maharashtra, IndiaAffiliation 2Karve NagarSavitribai Phule Pune UniversityPune, Maharashtra, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 08 Jun 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1698
- Pages:
- 1141–1152
Abstract
Keywords
Subject Classifications
References
[1] Busra Saylan , Songul Cinaroglu . Opinion mining and machine learning analysis : What emotions twitter data tell us about telemedicine?. Journal of Statistics & Management Systems ISSN 0972-0510 (Print), ISSN 2169-0014 (Online) Vol. 27, No. 3, pp. 605–633 (2024). DOI : 10.47974/JSMS-1028
[2] Huang, Minlie, et al. “Challenges in Building Intelligent Open-Domain Dialog Systems.” ACM Transactions on Information Systems, vol. 38, no. 3 (2020), https://doi.org/10.1145/3383123.
[3] Zhao, Deji, and Bo Ning. A Survey on Conversational Question-Answering Systems. Lecture Notes in Electrical Engineering, vol. 654 LNEE, no. 5, pp. 1857–61 (2021), https://doi.org/10.1007/978-981-15-8411-4_244.
[4] Pingle Aabha, et al. L3Cube-MahaSent-MD: A Multi-Domain Marathi Sentiment Analysis Dataset and Transformer Models (2023). arXiv:2306.13888v1https://doi.org/10.48550/arXiv.2306.13888.
[5] Yin Fulian, et al. “The Construction of Sentiment Lexicon Based on Context-Dependent Part-of-Speech Chunks for Semantic.” IEEE Access, vol. PP, p. 1 (2020), https://doi.org/10.1109/ACCESS.2020.2984284.
[6] Moeller Sarah, et al. “To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings.” ACL-IJCNLP 2021 - 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference, pp. 966–78 (2021), https://doi.org/10.18653/v1/2021.acl-long.78.
[7] Shah Sonali Rajesh, and Abhishek Kaushik. Sentiment analysis on indian indigenous languages : a review on multilingual opinion mining. preprints 2019110338 https://doi.org/10.20944/preprints201911.0338.v1.
[8] Ranathunga, Surangika, and Ranjiva Munasinghe. Indic Language Computing. no. June 2020 (2019), https://doi.org/10.1145/3343456.
[9] Lahoti, Pawan, et al. A Survey on NLP Resources , Tools , and Techniques For Marathi Language Processing. ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 22 Issue 2 Article No 4 7 pp 1–34. https://doi.org/10.1145/3548457.
[10] Kunchukuttan Anoop. GitHub - AI4Bharat/Indicnlp_catalog: A Collaborative Catalog of Resources for Indian Language NLP. https://github.com/AI4Bharat/indicnlp_catalog.30 March 2024.
[11] Mohammad Saif M., and Peter D. Turney. Crowdsourcing a Word-Emotion Association Lexicon. Computational Intelligence, vol. 29, no. 3, pp. 436–65 (2013), https://doi.org/10.1111/j.1467-8640.2012.00460.x.
[12] Mohammad Saif M., and Peter D. Turney. Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon. CAAGET ’10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, no.June, pp. 26–34 (2010).
[13] Baccianella, Stefano et al. SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. International Conference on Language Resources and Evaluation (2010).
[14] Das Amitava, and Sivaji Bandyopadhyay. “SentiWordNet for Indian Languages.” The 8th Workshop on Asian Language Resources (ALR), August, no. August, pp. 56–63 (2010), http://www.aclweb.org/anthology/W/W10/W10-3208.pdf.
[15] Aditya Joshi, Sagar Ahire, Pushpak Bhattacharyya, ‘Sentiment Resources: Lexicons and Datasets’, Book Chapter, ‘A Practical Guide to Sentiment Analysis’, Editors: Dr. Dipankar Das, Dr. Erik Cambria, Springer Publication
[16] Kulkarni, Atharva, Meet Mandhane, Manali Likhitkar, Gayatri Kshirsagar, and Raviraj Joshi. L3CubeMahaSent: A Marathi Tweet-Based Sentiment Analysis Dataset. April (2021), http://arxiv.org/abs/2103.11408.
[17] Wijayanti Rini, and Andria Arisal. Automatic Indonesian Sentiment Lexicon Curation with Sentiment Valence Tuning for Social Media Sentiment Analysis. ACM Transactions on Asian and Low-Resource Language Information Processing. Volume 20 Issue 1Article No.:15 pp 1–16 https://doi.org/10.1145/3425632
[18] Gatti Lorenzo, et al. SentiWords : Deriving a High Precision and High Coverage Lexicon for Sentiment Analysis. arXiv:1510.09079v1 no. 4, pp. 409–21 (2016). https://doi.org/10.1109/TAFFC.2015.2476456.
[19] Lai Siwei, et al. How to Generate a Good Word Embedding. arXiv:1507.05523v1 https://doi.org/10.48550/arXiv.1507.05523
[20] Li Minglei, et al. “Inferring Affective Meanings of Words from Word Embedding.” IEEE Transactions on Affective Computing, vol. 8, no. 4, pp. 443–56 (2017), https://doi.org/10.1109/TAFFC.2017.2723012.
[21] Altameem, Ayman, et al. “P-ROCK: a sustainable clustering algorithm for large categorical datasets.” Intell. Autom. Soft Comput 35.1: 553-566 ((2023)).
[22] D. Deng, L. Jing, J. Yu and S. Sun, “Sparse Self-Attention LSTM for Sentiment Lexicon Construction,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 11, pp. 1777-1790, Nov. (2019), doi: 10.1109/TASLP.2019.2933326.
[23] D. Deng, L. Jing, J. Yu, S. Sun and M. K. Ng, “Sentiment Lexicon Construction With Hierarchical Supervision Topic Model,” in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 4, pp. 704-718, April (2019), doi: 10.1109/TASLP.2019.2892232.
[24] O. Wu, T. Yang, M. Li and M. Li, “Two-Level LSTM for Sentiment Analysis With Lexicon Embedding and Polar Flipping,” in IEEE Transactions on Cybernetics, vol. 52, no. 5, pp. 3867-3879, May (2022), doi: 10.1109/TCYB.2020.3017378.
[25] Khan H. T, Ridhorkar Sonali. A pragmatic analysis of text-based sentiment analysis models from an analytical perspective. Journal of Statistics & Management Systems ISSN 0972-0510 (Print), ISSN 2169-0014 (Online) Vol. 27, No. 2, pp. 285–294 (2024). DOI: 10.47974/JSMS-1254 https://doi.org/10.47974/JS




