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 JCC  Vol.2 No.3 , February 2014
Twitter Sentiment in Data Streams with Perceptron
Abstract: With the huge increase in popularity of Twitter in recent years, the ability to draw information regarding public sentiment from Twitter data has become an area of immense interest. Numerous methods of determining the sentiment of tweets, both in general and in regard to a specific topic, have been developed, however most of these functions are in a batch learning environment where instances may be passed over multiple times. Since Twitter data in real world situations are far similar to a stream environment, we proposed several algorithms which classify the sentiment of tweets in a data stream. We were able to determine whether a tweet was subjective or objective with an error rate as low as 0.24 and an F-score as high as 0.85. For the determination of positive or negative sentiment in subjective tweets, an error rate as low as 0.23 and an F-score as high as 0.78 were achieved.
Cite this paper: Aston, N. , Liddle, J. and Hu, W. (2014) Twitter Sentiment in Data Streams with Perceptron. Journal of Computer and Communications, 2, 11-16. doi: 10.4236/jcc.2014.23002.
References

[1]   G. Preslav Nakov, Zornitsa Kozareva, Alan Ritter, Sara Rosenthal, Veselin Stoyanov, Theresa Wilson, “SemEval-2013 Task 2: Sentiment Analysis in Twitter,” Second Joint Conference on Lexical and Computational Semantics (*SEM), Seventh International Workshop on Semantic, Atlanta, 14-15 June 2013, Vol. 2, pp. 312-320.

[2]   L. Barbosa and J. L. Feng. “Robust Sentiment Detection on Twitter from Biased and Noisy Data,” Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010: Posters), Beijing, August 2010 pp. 36-44.

[3]   Y. H. Hu, F. Wang and S. Kambhampati, “Listening to the Crowd: Automated Analysis of Events via Aggregated Twitter Sentiment,” Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 2640-2646.

[4]   T. Carpenter and T. Way, “Tracking Sentiment Analysis through Twitter,” Proceedings of the 2012 International Conference on Information and Knowledge Engineering (IKE 2012), Las Vegas, 16-19 July 2012.

[5]   M. Hao, C. Rohrdantz, H. Janetzko, U. Dayal, D. A. Keim, L.-E. Haug and M.-C. Hsu. “Visual Sentiment Analysis on Twitter Data Streams,” IEEE Symposium on Visual Analytics Science and Technology, Providence, 23-28 October 2011.

[6]   L. Zhang, R. Ghosh, M. Dekhil, M. C. Hsu and B. Liu. “Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis,” HP Laboratories, 2011, HPL-2011-89.
http://www.hpl.hp.com/techreports/2011/HPL-2011-89.html

[7]   J. F. Si, A. Mukherjee, B. Liu, Q. Li, H. Y. Li and X. T. Deng, “Exploiting Topic Based Twitter Sentiment for Stock Prediction,” The 51st Annual Meeting of the Association for Computational Linguistics—Short Papers (ACL Short Papers 2013), Sofia, 4-9 August 2013.

[8]   G. Jo?o, “Knowledge Discovery from Data Streams,” Chapman & Hall/CRC, Boca Raton, 2010.

[9]   W. Deitrick and W. Hu, “Mutually Enhancing Community Detection and Sentiment Analysis on Twitter Networks,” Journal of Data Analysis and Information Processing, Vol. 1 No. 3, 2013, pp. 19-29.
http://dx.doi.org/10.4236/jdaip.2013.13004

[10]   H. Saif, Y. L. He and H. Alani, “Semantic Sentiment Analysis of Twitter,” The Semantic Web—ISWC 2012, Lecture Notes in Computer Science, Vol. 7649, pp. 508-524. http://dx.doi.org/10.1007/978-3-642-35176-1_32

[11]   Z. Miller, B. Dickinson, W. Deitrick, W. Hu and A. H. Wang, “Twitter Spammer Detection Using Data Stream Clustering,” Information Sciences, Vol. 260, 2014, pp. 64-73.

[12]   Y. Freund and R.E. Schapire, “Large Margin Classification Using the Perceptron Algorithm,” Machine Learning, Vol. 37, No. 3, 1999, pp. 277-296. http://dx.doi.org/10.1023/A:1007662407062

[13]   Semantria. 2013. https://semantria.com

 
 
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