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Sentiment Analysis of Twitter Data: A Survey of Techniques

by Vishal A. Kharde, S.S. Sonawane
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 139 - Number 11
Year of Publication: 2016
Authors: Vishal A. Kharde, S.S. Sonawane
10.5120/ijca2016908625

Vishal A. Kharde, S.S. Sonawane . Sentiment Analysis of Twitter Data: A Survey of Techniques. International Journal of Computer Applications. 139, 11 ( April 2016), 5-15. DOI=10.5120/ijca2016908625

@article{ 10.5120/ijca2016908625,
author = { Vishal A. Kharde, S.S. Sonawane },
title = { Sentiment Analysis of Twitter Data: A Survey of Techniques },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 139 },
number = { 11 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 5-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume139/number11/24532-2016908625/ },
doi = { 10.5120/ijca2016908625 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:40:38.456131+05:30
%A Vishal A. Kharde
%A S.S. Sonawane
%T Sentiment Analysis of Twitter Data: A Survey of Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 139
%N 11
%P 5-15
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics, have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide research on twitter data streams.We have also discussed general challenges and applications of Sentiment Analysis on Twitter.

References
  1. A.Pak and P. Paroubek. ‘Twitter as a Corpus for Sentiment Analysis and Opinion Mining". In Proceedings of the Seventh Conference on International Language Resources and Evaluation, 2010, pp.1320-1326
  2. R. Parikh and M. Movassate, “Sentiment Analysis of User- GeneratedTwitter Updates using Various Classi_cation Techniques",CS224N Final Report, 2009
  3. Go, R. Bhayani, L.Huang. “Twitter Sentiment ClassificationUsing Distant Supervision". Stanford University, Technical Paper,2009
  4. L. Barbosa, J. Feng. “Robust Sentiment Detection on Twitterfrom Biased and Noisy Data". COLING 2010: Poster Volume,pp. 36-44.
  5. Bifet and E. Frank, "Sentiment Knowledge Discovery inTwitter Streaming Data", In Proceedings of the 13th InternationalConference on Discovery Science, Berlin, Germany: Springer,2010, pp. 1-15.
  6. Agarwal, B. Xie, I. Vovsha, O. Rambow, R. Passonneau, “Sentiment Analysis of Twitter Data", In Proceedings of the ACL 2011Workshop on Languages in Social Media,2011 , pp. 30-38
  7. Dmitry Davidov, Ari Rappoport." Enhanced Sentiment Learning Using Twitter Hashtags and Smileys". Coling 2010: Poster Volumepages 241{249, Beijing, August 2010
  8. Po-Wei Liang, Bi-Ru Dai, “Opinion Mining on Social MediaData", IEEE 14th International Conference on Mobile Data Management,Milan, Italy, June 3 - 6, 2013, pp 91-96, ISBN: 978-1-494673-6068-5, http://doi.ieeecomputersociety.org/10.1109/MDM.2013.
  9. Pablo Gamallo, Marcos Garcia, “Citius: A Naive-Bayes Strategyfor Sentiment Analysis on English Tweets", 8th InternationalWorkshop on Semantic Evaluation (SemEval 2014), Dublin, Ireland,Aug 23-24 2014, pp 171-175.
  10. Neethu M,S and Rajashree R,” Sentiment Analysis in Twitter using Machine Learning Techniques” 4th ICCCNT 2013,at Tiruchengode, India. IEEE – 31661
  11. 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.
  12. J. Kamps, M. Marx, R. J. Mokken, and M. De Rijke, “Using wordnet to measure semantic orientations of adjectives,” 2004.
  13. 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.
  14. ZhunchenLuo, Miles Osborne, TingWang, An effective approachto tweets opinion retrieval", Springer Journal onWorldWideWeb,Dec 2013, DOI: 10.1007/s11280-013-0268-7.
  15. Liu, S., Li, F., Li, F., Cheng, X., &Shen, H.. Adaptive co-training SVM for sentiment classification on tweets. In Proceedings of the 22nd ACMinternational conference on Conference on information & knowledgemanagement (pp. 2079-2088). ACM,2013.
  16. Pan S J, Ni X, Sun J T, et al. “Cross-domain sentiment classification viaspectral feature alignment”. Proceedings of the 19th internationalconference on World wide web. ACM, 2010: 751-760.
  17. Wan, X..“A Comparative Study of Cross-Lingual SentimentClassification”. In Proceedings of the The 2012 IEEE/WIC/ACMInternational Joint Conferences on Web Intelligence and IntelligentAgent Technology-Volume 01 (pp. 24-31).IEEE Computer Society.2012
  18. Socher, Richard, et al. "Recursive deep models for semanticcompositionality over a sentiment Treebank." Proceedings of theConference on Empirical Methods in Natural Language Processing(EMNLP). 2013.
  19. Meng, Xinfan, et al. "Cross-lingual mixture model for sentimentclassification." Proceedings of the 50th Annual Meeting of theAssociation for Computational Linguistics Volume 1,2012
  20. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., &Stede, M..“Lexicon basedmethods for sentiment analysis”. Computational linguistics, 2011:37(2), 267-307.
  21. Li, S., Xue, Y., Wang, Z., & Zhou, G..“Active learning for cross-domainsentiment classification”. In Proceedings of the Twenty-Thirdinternational joint conference on Artificial Intelligence (pp. 2127-2133).AAAI Press,2013
  22. Bollegala, D., Weir, D., & Carroll, J.. Cross-Domain SentimentClassification using a Sentiment Sensitive Thesaurus. Knowledge andData Engineering, IEEE Transactions on, 25(8), 1719-1731,2013
  23. Pang, B.and Lee, L. “A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts”. 42nd Meeting of the Association for Computational Linguistics[C] (ACL-04). 2004, 271-278.
  24. 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

Twitter Sentiment analysis (SA) Opinion mining Machine learning Naive Bayes (NB) Maximum Entropy Support Vector Machine (SVM).

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