References
[1]. Chauchat, A. S. J. H., Eric, L., Lumière, U., & Mendèsfrance,
P. (2008). Opinion mining issues and agreement
identification in forum texts. In Workshop, Data Mining
Opinions (FODOP'08) Conjunction with the conference
INFORSID (pp. 51-58).
[2]. Chaudhari, C. V., Khaire, A. V., Murtadak, R. R., &
Sirsulla, K. S. (2017). Sentiment Analysis in Marathi using
Marathi WordNet. Imp J Interdiscip Res, 3(4), 1253-1256.
[3]. Deshmukh, S. Patil, N., Rotiwar, S., & Nunes, S. (2017).
Sentiment Analysis of Marathi Language. International
Journal of Research Publications in Engineering and
Technology, 3(6), 93-97.
[4]. Garapati, A., Bora, N., Balla, H., & Sai, M. (2019).
SentiPhraseNet: An extended SentiWordNet approach for
Telugu sentiment analysis. International Journal of
Advance Research, Ideas and Innovations in Technology,
5(2), 433-436.
[5]. Ghosal, T., Das, S. K., & Bhattacharjee, S. (2015,
December). Sentiment analysis on (bengali horoscope)
corpus. In 2015, Annual IEEE India Conference (INDICON)
(pp. 1-6). IEEE. https://doi.org/10.1109/INDICON.2015.744
3551
[6]. Hasan, K. A., & Rahman, M. (2014, December).
Sentiment detection from bangla text using contextual
th valency analysis. In 2014, 17 International Conference on
Computer and Information Technology (ICCIT) (pp. 292-
295). IEEE. https://doi.org/10.1109/ICCITechn.2014.70731
51
[7]. Hegde, Y., & Padma, S. K. (2015, June). Sentiment
analysis for Kannada using mobile product reviews: a case
study. In 2015, IEEE International Advance Computing
Conference (IACC) (pp. 822-827). IEEE. https://doi.org/10.
1109/IADCC.2015.7154821
[8]. Jha, V., Manjunath, N., Shenoy, P. D., Venugopal, K. R.,
& Patnaik, L. M. (2015, July). Homs: Hindi opinion mining
nd system. In 2015, IEEE 2 International Conference on
Recent Trends in Information Systems (ReTIS) (pp. 366-371).
IEEE. https://doi.org/10.1109/ReTIS.2015.7232906
[9]. Mukku, S. S., & Mamidi, R. (2017, September). Actsa:
Annotated corpus for telugu sentiment analysis. In
Proceedings of the First Workshop on Building Linguistically
Generalizable NLP Systems (pp. 54-58).
[10]. Mukku, S. S., Choudhary, N., & Mamidi, R. (2016).
Enhanced Sentiment Classification of Telugu Text using ML
Techniques. In 25th International Joint Conference on
Artificial Intelligence, 29-34.
[11]. Nair, D. S., Jayan, J. P., Rajeev, R. R., & Sherly, E. (2014,
September). SentiMa-sentiment extraction for Malayalam.
In 2014, International Conference on Advances in
Computing, Communications and Informatics (ICACCI)
(pp. 1719-1723). IEEE. https://doi.org/10.1109/ICACCI.20
14.6968548
[12]. Parupalli, S., Rao, V. A., & Mamidi, R. (2018). BCSAT: A
benchmark corpus for sentiment analysis in telugu using
word-level annotations. In 56th Annual Meeting of the
Association for Computational Linguistics, ACL.
[13]. Pawar, S. V., & Mali, S. (2017). Sentiment Analysis in
Marathi Language. International Journal on Recent and
Innovation Trends in Computing and Communication,
5(8), 21-25.
[14]. Pratibha, G., Hegde, N., Reddy, Ch. A., & Maneesh,
D. (2017). Parsing Sentiment in Telugu Language
Sentences. International Journal of Creative Research
Thoughts (IJCRT), 99-102.
[15]. Sitaram, D., Murthy, S., Ray, D., Sharma, D., & Dhar, K.
(2015, July). Sentiment analysis of mixed language
employing Hindi-English code switching. In 2015,
International Conference on Machine Learning and
Cybernetics (ICMLC) (Vol. 1, pp. 271-276). IEEE. https://doi.
org/10.1109/ICMLC.2015.7340934