Sentiment Analysis on the Social Networks Using Stream Algorithms
ABSTRACT
The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for identifying sentiment in OSNs such as communication pattern mining and classification based on emoticon and parts of speech, the majority of them utilize a suboptimal batch mode learning approach when analyzing a large amount of real time data. As an alternative we present a stream algorithm using Modified Balanced Winnow for sentiment analysis on OSNs. Tested on three real-world network datasets, the performance of our sentiment predictions is close to that of batch learning with the ability to detect important features dynamically for sentiment analysis in data streams. These top features reveal key words important to the analysis of sentiment.
The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for identifying sentiment in OSNs such as communication pattern mining and classification based on emoticon and parts of speech, the majority of them utilize a suboptimal batch mode learning approach when analyzing a large amount of real time data. As an alternative we present a stream algorithm using Modified Balanced Winnow for sentiment analysis on OSNs. Tested on three real-world network datasets, the performance of our sentiment predictions is close to that of batch learning with the ability to detect important features dynamically for sentiment analysis in data streams. These top features reveal key words important to the analysis of sentiment.
KEYWORDS
Modified Balanced Winnow, Sentiment Analysis, Twitter, Online Social Networks, Feature Selection, Data Streams
Modified Balanced Winnow, Sentiment Analysis, Twitter, Online Social Networks, Feature Selection, Data Streams
Cite this paper
Aston, N. , Munson, T. , Liddle, J. , Hartshaw, G. , Livingston, D. and Hu, W. (2014) Sentiment Analysis on the Social Networks Using Stream Algorithms. Journal of Data Analysis and Information Processing, 2, 60-66. doi: 10.4236/jdaip.2014.22008.
Aston, N. , Munson, T. , Liddle, J. , Hartshaw, G. , Livingston, D. and Hu, W. (2014) Sentiment Analysis on the Social Networks Using Stream Algorithms. Journal of Data Analysis and Information Processing, 2, 60-66. doi: 10.4236/jdaip.2014.22008.
References
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http://dx.doi.org/10.1016/B978-012395173-1/50002-5 [2] Deitrick, W. and Hu, W. (2013) Mutually Enhancing Community Detection and Sentiment Analysis on Twitter Networks. Journal of Data Analysis and Information Processing, 1, 19-29.
http://dx.doi.org/10.4236/jdaip.2013.13004 [3] Pfitzner, R., Garas, A. and Schweitzer, F. (2012) Emotional Divergence Influences Information Spreading in Twitter. ICWSM-12. [4] Garas, A., Garcia, D., Skowron, M. and Schweitzer, F. (2012) Emotional Persistence in Online Chatting Communities. Scientific Reports, 2, Article Number: 402. http://dx.doi.org/10.1038/srep00402 [5] Thelwall, M., et al. (2010) Sentiment Strength Detection in Short Informal Text. Journal of the American Society for Information Science and Technology, 61, 2544-2558.
http://dx.doi.org/10.1002/asi.21416 [6] Pak, Alexander, and Patrick Paroubek (2010) Twitter as a Corpus for Sentiment Analysis and Opinion Mining. LREC 2010. [7] Go, A., Bhayani, R. and Huang, L. (2009) Twitter Sentiment Classification Using Distant Supervision. CS224N Project Report, 1-12. [8] Schmid, H. (1994) Probabilistic Part-of-Speech Tagging Using Decision Trees. Proceedings of International Conference on New Methods in Language Processing, Vol. 12. [9] Aston, N., Liddle, J. and Hu, W. (2014) Twitter Sentiment in Date Streams with Perceptron. Journal of Computer and Communications, 2, 11-16. http://dx.doi.org/10.4236/jcc.2014.23002 [10] Joa?o, G. (2010) Knowledge Discovery from Date Streams. Taylor & Francis, Boca Raton. [11] Bifet, Albert, and Eibe Frank. (2010) Sentiment Knowledge Discovery in Twitter Streaming Data. Proceedings of 13th International Conference on Discovery Science, Canberra, 6-8 October 2010, 1-15. [12] Sanders, Niek. Twitter Sentiment Corpus. Sanders Analytics.
http://www.sananalytics.com/lab/twitter-sentiment/ [13] Saif, Hassan. Tweenator. http://www.tweenator.com/index.php?page_id=13 [14] Carvalho, V.R. and William, W.C. (2006) Single-Pass Online Learning: Performance, Voting Schemes and Online Feature Selection. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, 20-23 August 2006, 548-553.
[1] Coffman, K.G. and Odlyzko, A.M. (2002) Growth of the Internet. In: Kaminow, I.P. and Li, T., Eds., Optical Fiber Telecommunications IV B: Systems and Impairments, Academic Press, San Diego, 17-56.
http://dx.doi.org/10.1016/B978-012395173-1/50002-5 [2] Deitrick, W. and Hu, W. (2013) Mutually Enhancing Community Detection and Sentiment Analysis on Twitter Networks. Journal of Data Analysis and Information Processing, 1, 19-29.
http://dx.doi.org/10.4236/jdaip.2013.13004 [3] Pfitzner, R., Garas, A. and Schweitzer, F. (2012) Emotional Divergence Influences Information Spreading in Twitter. ICWSM-12. [4] Garas, A., Garcia, D., Skowron, M. and Schweitzer, F. (2012) Emotional Persistence in Online Chatting Communities. Scientific Reports, 2, Article Number: 402. http://dx.doi.org/10.1038/srep00402 [5] Thelwall, M., et al. (2010) Sentiment Strength Detection in Short Informal Text. Journal of the American Society for Information Science and Technology, 61, 2544-2558.
http://dx.doi.org/10.1002/asi.21416 [6] Pak, Alexander, and Patrick Paroubek (2010) Twitter as a Corpus for Sentiment Analysis and Opinion Mining. LREC 2010. [7] Go, A., Bhayani, R. and Huang, L. (2009) Twitter Sentiment Classification Using Distant Supervision. CS224N Project Report, 1-12. [8] Schmid, H. (1994) Probabilistic Part-of-Speech Tagging Using Decision Trees. Proceedings of International Conference on New Methods in Language Processing, Vol. 12. [9] Aston, N., Liddle, J. and Hu, W. (2014) Twitter Sentiment in Date Streams with Perceptron. Journal of Computer and Communications, 2, 11-16. http://dx.doi.org/10.4236/jcc.2014.23002 [10] Joa?o, G. (2010) Knowledge Discovery from Date Streams. Taylor & Francis, Boca Raton. [11] Bifet, Albert, and Eibe Frank. (2010) Sentiment Knowledge Discovery in Twitter Streaming Data. Proceedings of 13th International Conference on Discovery Science, Canberra, 6-8 October 2010, 1-15. [12] Sanders, Niek. Twitter Sentiment Corpus. Sanders Analytics.
http://www.sananalytics.com/lab/twitter-sentiment/ [13] Saif, Hassan. Tweenator. http://www.tweenator.com/index.php?page_id=13 [14] Carvalho, V.R. and William, W.C. (2006) Single-Pass Online Learning: Performance, Voting Schemes and Online Feature Selection. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, 20-23 August 2006, 548-553.