ARCHIVES

1. Das, A., & Bandyopadhyay, S. (2010). SentiWordNet for Indian languages. Proceedings of the 8th International Conference on Natural
Language Processing (ICON)
2. Hutto, C., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of
the 8th International AAAI Conference on Weblogs and Social Media (ICWSM).
3. Joshi, P., Santy, S., Budhiraja, A., Bali, K., & Choudhury, M. (2020). The state and fate of linguistic diversity and inclusion in the NLP
world.Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL).
4. A. Das and S. Bandyopadhyay, “SentiWordNet for Bangla,” Proc. 8th Workshop on Asian Language Resources (Coling 2010), Beijing,
China, pp. 56–63, 2010.
5. A. Joshi, A. R. Balamurali, and P. Bhattacharyya, “A fall-back strategy for sentiment analysis in Hindi: A case study,” Proc. 8th
ICON: International Conference on Natural Language Processing, pp. 149–153, 2010.
6. S. Mukku and R. Mamidi, “Sentiment analysis on Telugu tweets using machine learning approaches,” Proc. 14th Int. Conf. on Natural
Language Processing (ICON-2017), pp. 211–216, 2017.
7. D. Sharma, P. Kaur, and M. Singh, “Machine learning-based sentiment analysis for Punjabi text,” International Journal of Computer
Applications, vol. 179, no. 3, pp. 1–5, 2018.
8. A. McCallum and K. Nigam, “A comparison of event models for Naive Bayes text classification,” AAAI-98 Workshop on Learning
for Text Categorization, pp. 41–48, 1998.
9. B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques,” Proc. ACL-02 Conf. on
Empirical Methods in Natural Language Processing, pp. 79–86, 2002.
10. A. Kirbriya, X. Li, and W. W. Cohen, “An empirical comparison of supervised learning algorithms for text categorization,” Proc. 22nd Int.
Conf. on Machine Learning (ICML-04), pp. 163–170, 2004.
11. A. Sharma, J. Deka, and M. Das, “Part-of-speech tagging for Assamese using Hidden Markov Model,” Proc. 3rd Workshop on South and
Southeast Asian Natural Language Processing (SANLP 2012), pp. 95–102, 2012.
12. R. Sarma and S. Sarma, “Building Assamese corpus for NLP applications,” International Journal on Natural Language Computing, vol. 5,
no. 2,pp. 1–10, 2016.
13. A. Pathak, R. Sarma, and P. Barman, “Statistical machine translation between Assamese and English,” International Journal of
Computer Applications, vol. 180, no. 28, pp. 19–24, 2018.
14. A. Balahur and M. Turchi, “Comparative experiments using supervised learning and machine translation for multilingual sentiment
analysis,”Computer Speech & Language, vol. 28, no. 1, pp. 56–75, 2014.
15. P. Lohar, A. Jha, and A. Mishra, “Impact of machine translation on sentiment classification,” Procedia Computer Science, vol. 122, pp. 839–
846, 2017.
16. P. Joshi, S. Santy, A. Budhiraja, K. Bali, and M. Choudhury, “The state and fate of linguistic diversity and inclusion in the NLP world,” Proc.
58th Annual Meeting of the Association for Computational Linguistics, pp. 6282–6293, 2020