ISSN : 2583-2646

Twitter Sentimental Analysis

ESP Journal of Engineering & Technology Advancements
© 2021 by ESP JETA
Volume 1  Issue 1
Year of Publication : 2021
Authors : John K.Victor, Ilo stanely Uzochukwu, Dr.N.Egu
: 10.56472/25832646/ESP-V1I1P101

Citation:

John K.Victor, Ilo stanely Uzochukwu, Dr.N.Egu, 2021. "Twitter Sentimental Analysis" ESP Journal of Engineering & Technology Advancements  1(1): 1-5.

Abstract:

Web-based media have gotten more consideration. The best example of web-based media is a Twitter that is used for acquiring fame. Twitter offers associations a quick and viable approach to dissect clients' points of view toward the basic to accomplishment in the commercial center. Fostering a program for supposition examination is a way to deal with be utilized to quantify clients' insights computationally. This clarifies the plan of an opinion investigation, separating an immense measure of tweets. Results group clients' viewpoint employing tweets into positive, negative, and nonpartisan, which is addressed in a pie outline and html page.

References:

[1] A.Pak and P. Paroubek. 2010. "Twitter as a Corpus for Sentiment Analysis and Opinion Mining". In Proceedings of the Seventh Conference on International Language Resources and Evaluation, 1320-1326
[2] R. Parikh and M. Movassate, 2009. "Sentiment Analysis of User- Generated Twitter Updates using Various Classi_cation Techniques", CS224N Final Report.
[3] Go, R. Bhayani, L.Huang. 2009. "Twitter Sentiment ClassificationUsing Distant Supervision". Stanford University, Technical Paper.
[4] L. Barbosa, J. Feng. "Robust Sentiment Detection on Twitter from Biased and Noisy Data".COLING 2010: Poster Volume, 36-44.
[5] Bifet and E. Frank, 2010. "Sentiment Knowledge Discovery in Twitter Streaming Data", In Proceedings of the 13th InternationalConference on Discovery Science, Berlin, Germany: Springer, 1-15. ESP Journal of Engineering and Technology Advancements 6
[6] Agarwal, B. Xie, I. Vovsha, O. Rambow, R. Passonneau, 2011. "Sentiment Analysis of Twitter Data", In Proceedings of the ACL 2011Workshop on Languages in Social Media, 30-38
[7] Dmitry Davidov, Ari Rappoport. 2010. "Enhanced Sentiment Learning Using Twitter Hashtags and Smileys". Coling 2010: Poster Volume pages 241{249, Beijing.
[8] Po-Wei Liang, Bi-Ru Dai, 2013. "Opinion Mining on Social MediaData", IEEE 14th International Conference on Mobile Data Management, Milan, Italy, 91-96, ISBN: 978-1-494673-6068-5, http://doi.ieeecomputersociety.org/10.1109/MDM.2013.
[9] Pablo Gamallo, Marcos Garcia, 2014. "Citius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets", 8th international workshop on Semantic Evaluation (SemEval 2014), Dublin, Ireland, 171-175.
[10] Neethu M, S and Rajashree R," Sentiment Analysis in Twitter using Machine Learning Techniques" 4th ICCCNT 2013,atTiruchengode, India. IEEE – 31661.

Keywords:

twitter, architecture