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Twitter Sentiment Analysis using Machine Learning and Knowledge-based Approach

by Riya Suchdev, Pallavi Kotkar, Rahul Ravindran, Sridhar Swamy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 103 - Number 4
Year of Publication: 2014
Authors: Riya Suchdev, Pallavi Kotkar, Rahul Ravindran, Sridhar Swamy
10.5120/18066-9006

Riya Suchdev, Pallavi Kotkar, Rahul Ravindran, Sridhar Swamy . Twitter Sentiment Analysis using Machine Learning and Knowledge-based Approach. International Journal of Computer Applications. 103, 4 ( October 2014), 36-40. DOI=10.5120/18066-9006

@article{ 10.5120/18066-9006,
author = { Riya Suchdev, Pallavi Kotkar, Rahul Ravindran, Sridhar Swamy },
title = { Twitter Sentiment Analysis using Machine Learning and Knowledge-based Approach },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 103 },
number = { 4 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume103/number4/18066-9006/ },
doi = { 10.5120/18066-9006 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:34:54.212188+05:30
%A Riya Suchdev
%A Pallavi Kotkar
%A Rahul Ravindran
%A Sridhar Swamy
%T Twitter Sentiment Analysis using Machine Learning and Knowledge-based Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 103
%N 4
%P 36-40
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentiment analysis is mainly concerned with identifying and classifying opinions or emotions that are expressed within a text. These days, sharing opinions and expressing emotions through social networking websites has become very common. Therefore, a large amount of data is generated each day, on which mining can be effectively performed to retrieve quality information. Sentiment analysis on such data can prove to be instrumental in generating an aggregated opinion on certain products. Twitter sentiment analysis often becomes a difficult task due to the presence of slangs and misspellings. Also, we constantly encounter new words, which makes it more difficult to analyze and compute the sentiment as compared to the usual sentiment analysis. Twitter restricts the length of a tweet to 140 characters. Thus, extracting valuable information from short texts is yet another challenge. Knowledge-based approach and machine learning can contribute considerably towards the analysis of sentiments from tweets. In this paper, we analyze people's sentiments in their tweets about certain companies. Computing a basic sentiment score and then classifying them as positive or negative would help to serve the company by providing them critical reviews about their products from people worldwide.

References
  1. Bahrainian, S. A. , Bahrainian A. M, Salarinasab, M. , and Dengel, A. ,Implementation of an Intelligent Product Recommender System in an e-Store, Proceedings AMT-10, Int'l Conference on Active Media Technology, Toronto, Canada, 2010.
  2. Mejova, Y. (2009). Sentiment Analysis: An Overview. Comprehensive exam paper, available on http://www. cs. uiowa. edu/~ymejova/publications/CompsYelenaMejova. pdf [2010-02-03].
  3. P. D. Turney, "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews," in Proceedings of the 40th annual meeting on association for computational linguistics ,pp. 417–424, Association for Computational Linguistics, 2002.
  4. J. Kamps, M. Marx, R. J. Mokken, and M. De Rijke, "Using wordnet to measure semantic orientations of adjectives," 2004.
  5. C. Fellbaum, "Wordnet: An electronic lexical database (language,speech, and communication)," 1998.
  6. D. Pucci, M. Baroni, F. Cutugno, and A. Lenci, "Unsupervised lexical substitution with a word space model," in Proceedings of EVALITA workshop, 11th Congress of Italian Association for Artificial Intelligence,Citeseer, 2009.
  7. A. Balahur, J. M. Hermida, and A. Montoyo, "Building and exploiting emotinet, a knowledge base for emotion detection based on the appraisal theory model," Affective Computing, IEEE Transactions on, vol. 3, no. 1,pp. 88–101, 2012.
  8. C. Strapparava and R. Mihalcea, "Semeval-2007 task 14: Affective text," in Proceedings of the 4th International Workshop on Semantic Evaluations, pp. 70–74, Association for Computational Linguistics, 2007.
  9. G. Vinodhini and R. Chandrasekaran, "Sentiment analysis and opinion mining: A survey,"International Journal, vol. 2, no. 6, 2012.
  10. P. Domingos and M. Pazzani, "On the optimality of the simple bayesian classifier under zero-one loss," Machine Learning, vol. 29, no. 2-3, pp. 103–130, 1997.
  11. Z. Niu, Z. Yin, and X. Kong, "Sentiment classification for microblog by machine learning," in Computational and Information Sciences (ICCIS),2012 Fourth International Conference on, pp. 286–289, IEEE, 2012.
  12. L. Barbosa and J. Feng, "Robust sentiment detection on twitter from biased and noisy data," in Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 36–44, Association for Computational Linguistics, 2010.
  13. A. Celikyilmaz, D. Hakkani-Tur, and J. Feng, "Probabilistic model-based sentiment analysis of twitter messages," in Spoken Language Technology Workshop (SLT), 2010 IEEE, pp. 79–84, IEEE, 2010.
  14. Y. Wu and F. Ren, "Learning sentimental influence in twitter," in Future Computer Sciences and Application (ICFCSA), 2011 International Conference on, pp. 119–122, IEEE, 2011.
  15. A. Pak and P. Paroubek, "Twitter as a corpus for sentiment analysis and opinion mining," in Proceedings of LREC, vol. 2010, 2010.
  16. R. Xia, C. Zong, and S. Li, "Ensemble of feature sets and classification algorithms for sentiment classification," Information Sciences: an International Journal, vol. 181, no. 6, pp. 1138–1152, 2011.
  17. V. M. K. Peddinti and P. Chintalapoodi, "Domain adaptation in sentiment analysis of twitter," in Analyzing Microtext Workshop, AAAI, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment analysis Knowledge-based approach Machine Learning Twitter Sentiment score

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