Competitive intelligence in social media Twitter: iPhone 6 vs. Galaxy S5

Purpose – The purpose of this paper is to mine competitive intelligence in social media to find the market insight by comparing consumer opinions and sales performance of a business and one of its competitors by analyzing the public social media data. Design/methodology/approach – An exploratory test using a multiple case study approach was used to compare two competing smartphone manufacturers. Opinion mining and sentiment analysis are conducted first, followed by further validation of results using statistical analysis. A total of 229,948 tweets mentioning the iPhone6 or the GalaxyS5 have been collected for four months following the release of the iPhone6; these have been analyzed using natural language processing, lexicon-based sentiment analysis, and purchase intention classification. Findings – The analysis showed that social media data contain competitive intelligence. The volume of tweets revealed a significant gap between the market leader and one follower; the purchase intention data also reflect...

[1]  Weiguo Fan,et al.  The power of social media analytics , 2014, CACM.

[2]  Jennifer Jie Xu,et al.  Business Intelligence in Blogs: Understanding Consumer Interactions and Communities , 2012, MIS Q..

[3]  Jin-Man Kim,et al.  An Empirical Comparison of Machine Learning Models for Classifying Emotions in Korean Twitter , 2014 .

[4]  Fei-Yue Wang Really Artificial or Artificially Real? , 2010, IEEE Intell. Syst..

[5]  Björn W. Schuller,et al.  New Avenues in Opinion Mining and Sentiment Analysis , 2013, IEEE Intelligent Systems.

[6]  Wu He,et al.  International Journal of Information Management Social Media Competitive Analysis and Text Mining: a Case Study in the Pizza Industry , 2022 .

[7]  Chang-Tien Lu,et al.  Analyzing Civil Unrest through Social Media , 2013, Computer.

[8]  Q. Ye,et al.  The impact of e-word-of-mouth on the online popularity of restaurants: a comparison of consumer reviews and editor reviews. , 2010 .

[9]  Wei Chen,et al.  The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings , 2011, Comput. Hum. Behav..

[10]  Garrett P. Sonnier,et al.  A Dynamic Model of the Effect of Online Communications on Firm Sales , 2011, Mark. Sci..

[11]  Hsinchun Chen,et al.  Business and Market Intelligence 2.0, Part 2 , 2010, IEEE Intelligent Systems.

[12]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[13]  Rosa M. Carro,et al.  Sentiment analysis in Facebook and its application to e-learning , 2014, Comput. Hum. Behav..

[14]  Yongjun Sung,et al.  Brand followers' retweeting behavior on Twitter: How brand relationships influence brand electronic word-of-mouth , 2014, Comput. Hum. Behav..

[15]  Wu He,et al.  A novel social media competitive analytics framework with sentiment benchmarks , 2015, Inf. Manag..

[16]  R. Lusch,et al.  User-Generated Content on Social Media: Predicting Market Success with Online Word-of-Mouth , 2010 .

[17]  Larry Kahaner,et al.  Competitive Intelligence: How to Gather Analyze and Use Information to Move Your Business to the Top , 1996 .

[18]  Oliver Brdiczka,et al.  A comparison study of user behavior on Facebook and Gmail , 2013, Comput. Hum. Behav..

[19]  Efthimios Tambouris,et al.  Understanding the Predictive Power of Social Media This is a pre-print version of the following article : , 2013 .

[20]  Hsinchun Chen,et al.  AI and Opinion Mining , 2010, IEEE Intelligent Systems.

[21]  Cong Li,et al.  Twitter as a social actor: How consumers evaluate brands differently on Twitter based on relationship norms , 2014, Comput. Hum. Behav..

[22]  Seung Ryul Jeong,et al.  Opinion-Mining Methodology for Social Media Analytics , 2015, KSII Trans. Internet Inf. Syst..

[23]  Hsinchun Chen,et al.  Social Media Analytics and Intelligence , 2010, IEEE Intell. Syst..

[24]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[25]  Chihli Hung,et al.  Using Objective Words in SentiWordNet to Improve Word-of-Mouth Sentiment Classification , 2013, IEEE Intelligent Systems.

[26]  David J. Faulds,et al.  Social media: The new hybrid element of the promotion mix , 2009 .

[27]  Lihua Huang,et al.  Promotional Marketing or Word-of-Mouth? Evidence from Online Restaurant Reviews , 2013 .

[28]  R. Yin Case Study Research: Design and Methods , 1984 .

[29]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[30]  Bruno S. Silvestre,et al.  Social Media? Get Serious! Understanding the Functional Building Blocks of Social Media , 2011 .

[31]  Yubo Chen,et al.  The Phase Transition of Markets and Organizations: The New Intelligence and Entrepreneurial Frontier , 2010 .

[32]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[33]  Andrew B. Whinston,et al.  Whose and what chatter matters? The effect of tweets on movie sales , 2013, Decis. Support Syst..

[34]  Seung Ryul Jeong,et al.  Comparing Machine Learning Classifiers for Movie WOM Opinion Mining , 2015, KSII Trans. Internet Inf. Syst..

[35]  Hong Zhou,et al.  OpinionSeer: Interactive Visualization of Hotel Customer Feedback , 2010, IEEE Transactions on Visualization and Computer Graphics.

[36]  Jennifer E. Rowley,et al.  Managing brand presence through social media: the case of UK football clubs , 2014, Internet Res..