A Method to Divide Stream Data of Scores over Review Sites

The word of mouth information over certain review sites affects various activities from person to person. In large-scale review sites, it can happen that evaluation tendency of a product changes in a large way by only a few reviews that were rated and posted by certain users. Thus, it is very important to be able to detect those influential reviews in social media analysis. We propose an algorithm that can efficiently divide stream data of review scores by maximizing the likelihood of generating the observed sequence data. We assume that the user’s fundamental scoring behavior follows a multinomial distribution model and formulate a division problem.

[1]  Hsinchun Chen,et al.  Burst Detection From Multiple Data Streams: A Network-Based Approach , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Dennis Shasha,et al.  Efficient elastic burst detection in data streams , 2003, KDD '03.

[3]  Lifeng Sun,et al.  Item-Level Social Influence Prediction with Probabilistic Hybrid Factor Matrix Factorization , 2011, AAAI.

[4]  Hakim Hacid,et al.  A predictive model for the temporal dynamics of information diffusion in online social networks , 2012, WWW.

[5]  Richard Colbaugh,et al.  Estimating sentiment orientation in social media for intelligence monitoring and analysis , 2010, 2010 IEEE International Conference on Intelligence and Security Informatics.

[6]  Patrick Paroubek,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2010, LREC.

[7]  Duncan J. Watts,et al.  Everyone's an influencer: quantifying influence on twitter , 2011, WSDM '11.

[8]  Scott Counts,et al.  Predicting the Speed, Scale, and Range of Information Diffusion in Twitter , 2010, ICWSM.

[9]  Masahiro Kimura,et al.  Detecting changes in content and posting time distributions in social media , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[10]  Richard Colbaugh,et al.  Estimating Sentiment Orientation in Social Media for Business Informatics , 2011, AAAI Spring Symposium: AI for Business Agility.

[11]  James Allan,et al.  Automatic generation of overview timelines , 2000, SIGIR '00.

[12]  Prem Melville,et al.  Sentiment analysis of blogs by combining lexical knowledge with text classification , 2009, KDD.

[13]  Jure Leskovec,et al.  Modeling Information Diffusion in Implicit Networks , 2010, 2010 IEEE International Conference on Data Mining.