Development of a Natural Language Processing System using the concepts of Machine Learning
Abstract
Sentiment Analysis is a branch of Natural Language Processing, which can be described as the process of fortitude of the emotional tone signified by a series of words, which are used to discern the opinion of the writer. In this paper, we introduce the development of Methodology for the Sentiment Analysis of Kannada Movie Reviews using Machine Learning utilizing the concepts of Natural Language Processing (NLP). The model results show the effectualness of the method created using programing skills.
How to cite this article:
Jipeng T, Neelagar MB, Prasad AN et al. Development of a Natural Language Processing System using the concepts of Machine Learning. J Adv Res Embed Sys 2020; 7(3&4): 29-31.
References
[1] Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science 2(1), 1–8 (2011)
[2] Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: What 140 characters reveal about political sentiment. ICWSM, Washington, DC 10, 178–185 (2010)
[3] Nagy, A., Stamberger, J.: Crowd sentiment detection during disasters and crises. In: Proceedings of the 9th International ISCRAM Conference, Vancouver, Canada. pp. 1–9 (2012)
[4] Deepu S. Nair, Jisha P. Jayan, Rajeev R.R, Elizabeth Sherly,” Senti Ma-Sentiment Extraction for Malayalam”, 2014.
[5] ] B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis”, Foundations and Trends in Information Retrieval, vol. 2, no. 1–2, pp. 1–135 (2008).
[6] Amitava Das, SivajiBandopadaya, SentiWordNet for Bangla, Knowledge Sharing Event -4: Task, Volume 2, 2010.
[7] Yakshi Sharma, VeenuMangat, MandeepKaur, A practical Approach to Semantic Analysis of Hindi tweets”, 1st International Conference on Next Generation Computing Technologies(NGCT-2015), Dehradun, India,Page No(677-680), September 4-5, 2015.
[8] Vijayalaxmi F. Patil, Designing POS Tagset for Kannada, LDC-IL, CIIL Mysore.
[9] Cross Language POS Taggers (and other Tools) for Indian Languages: An Experiment with Kannada using Telugu Resources by Siva Reddy
[2] Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: What 140 characters reveal about political sentiment. ICWSM, Washington, DC 10, 178–185 (2010)
[3] Nagy, A., Stamberger, J.: Crowd sentiment detection during disasters and crises. In: Proceedings of the 9th International ISCRAM Conference, Vancouver, Canada. pp. 1–9 (2012)
[4] Deepu S. Nair, Jisha P. Jayan, Rajeev R.R, Elizabeth Sherly,” Senti Ma-Sentiment Extraction for Malayalam”, 2014.
[5] ] B. Pang and L. Lee, “Opinion Mining and Sentiment Analysis”, Foundations and Trends in Information Retrieval, vol. 2, no. 1–2, pp. 1–135 (2008).
[6] Amitava Das, SivajiBandopadaya, SentiWordNet for Bangla, Knowledge Sharing Event -4: Task, Volume 2, 2010.
[7] Yakshi Sharma, VeenuMangat, MandeepKaur, A practical Approach to Semantic Analysis of Hindi tweets”, 1st International Conference on Next Generation Computing Technologies(NGCT-2015), Dehradun, India,Page No(677-680), September 4-5, 2015.
[8] Vijayalaxmi F. Patil, Designing POS Tagset for Kannada, LDC-IL, CIIL Mysore.
[9] Cross Language POS Taggers (and other Tools) for Indian Languages: An Experiment with Kannada using Telugu Resources by Siva Reddy
Published
2020-07-23
How to Cite
PRASAD N, Achyutha.
Development of a Natural Language Processing System using the concepts of Machine Learning.
Journal of Advanced Research in Embedded System, [S.l.], v. 7, n. 3 & 4, p. 29-31, july 2020.
ISSN 2395-3802.
Available at: <http://thejournalshouse.com/index.php/ADR-Journal-Embedded-Systems/article/view/201>. Date accessed: 02 may 2024.
Section
Articles
