Social intelligence framework: Extracting and analyzing opinions for social CRM

The increasing number of people using social media to express their personal experiences has led to an emerging interest in supporting social media analysis for marketing, opinion analysis and understanding community cohesion. Social customer relationship management (SCRM) systems have become an interesting research area. Generally analysis needs to be done on large volumes of data (Big Data) in an efficient and timely manner. In this research, we propose a social intelligence framework that can extract and consolidate the reviews expressed via social media to help enterprises to know more about the customers' opinion toward the target products. This goal can be achieved by analyzing the reviewers' knowledge and authority and their opinion, sentiment (SentiGem) toward the target products, after filtering the extracted tweets from Twitter using Twitter4J. Additionally we present an architecture based on Map/Reduce analysis using Hadoop.

[1]  Yung-Ming Li,et al.  Creating social intelligence for product portfolio design , 2014, Decis. Support Syst..

[2]  Xiaohui Yu,et al.  ARSA: a sentiment-aware model for predicting sales performance using blogs , 2007, SIGIR.

[3]  Chih-Ping Wei,et al.  To whom should I listen? Finding reputable reviewers in opinion-sharing communities , 2012, Decis. Support Syst..

[4]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[5]  Rashid Ali,et al.  Social Network Extraction: A Review of Automatic Techniques , 2014 .

[6]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[7]  Brendan T. O'Connor,et al.  From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series , 2010, ICWSM.

[8]  F. Ashcroft,et al.  VIII. References , 1955 .

[9]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[10]  Mary J. Culnan,et al.  How Large U.S. Companies Can Use Twitter and Other Social Media to Gain Business Value , 2010, MIS Q. Executive.

[11]  Hemant Kumar Singh,et al.  Web Data Mining research: A survey , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[12]  Kôiti Hasida,et al.  POLYPHONET: An advanced social network extraction system from the Web , 2007, J. Web Semant..

[13]  Mike Thelwall,et al.  Sentiment strength detection for the social web , 2012, J. Assoc. Inf. Sci. Technol..

[14]  Jure Leskovec,et al.  Sentiment Flow Through Hyperlink Networks , 2011, ICWSM.

[15]  Saif Mohammad,et al.  Tracking Sentiment in Mail: How Genders Differ on Emotional Axes , 2011, WASSA@ACL.

[16]  Bart Selman,et al.  The Hidden Web , 1997, AI Mag..

[17]  Peter Mika,et al.  Flink: Semantic Web technology for the extraction and analysis of social networks , 2005, J. Web Semant..

[18]  I-Hsien Ting,et al.  Analyzing Multi-source Social Data for Extracting and Mining Social Networks , 2009, 2009 International Conference on Computational Science and Engineering.

[19]  Aditya G. Parameswaran,et al.  Blogs as Predictors of Movie Success , 2009, ICWSM.

[20]  Saif Mohammad,et al.  From once upon a time to happily ever after: Tracking emotions in mail and books , 2012, Decis. Support Syst..

[21]  Steffen Staab,et al.  Social Networks Applied , 2005, IEEE Intell. Syst..

[22]  I-Hsien Ting,et al.  A Dynamic and Task-Oriented Social Network Extraction System Based on Analyzing Personal Social Data , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.

[23]  Jimeng Sun,et al.  DisCo: Distributed Co-clustering with Map-Reduce: A Case Study towards Petabyte-Scale End-to-End Mining , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[24]  Natalie S. Glance,et al.  Star Quality: Aggregating Reviews to Rank Products and Merchants , 2010, ICWSM.

[25]  J. Sheth,et al.  Customer Relationship Management: Emerging Practice, Process, and Discipline , 2002 .

[26]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[27]  Mitsuru Ishizuka,et al.  Extracting Social Networks Among Various Entities on the Web , 2007, ESWC.

[28]  Bernardo A. Huberman,et al.  Predicting the Future with Social Media , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[29]  Noah A. Smith,et al.  Movie Reviews and Revenues: An Experiment in Text Regression , 2010, NAACL.

[30]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[31]  Noah A. Smith,et al.  What's Worthy of Comment? Content and Comment Volume in Political Blogs , 2010, ICWSM.

[32]  Maria Teresa Gomez Lopez,et al.  COMPETITIVE INTELLIGENCE BASED ON SOCIAL NETWORKS FOR DECISION MAKING , 2009 .