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Analyzingcustomersentiments in microblogs:A topic-model-basedapproachforTwitterdatasets,[object Object],Amercian Conference on Information Systems, Detroit, 2011,[object Object],Stefan Sommer, Andreas Schieber,[object Object],Twitterbird: http://www.flickr.com/photos/bertop/3193626407/,[object Object]
Imagineyourare a productmanagerof Sony TVs:Whatistheconversationabout?,[object Object],Buy a 3D TV,[object Object],08/06/2011,[object Object],Sommer, Schieber / Analyzingcustomersentiments in microblogs,[object Object],2,[object Object],Sentiments, opinions,[object Object],Sony TV: http://www.sony.de/hub/lcd-fernseher/produktpalette/3d,[object Object],Communication plattformsofthe Web 2.0,[object Object]
The communication plattform Twitter:What am I doing?,[object Object],Microblog - Ordinaryblogwithsocialnetworkingfeatures (followingotherusers),[object Object],Eachentryis limited to 140 characters,[object Object],Mark wordswithhashtags (#), Adressuserswith@, link informationwithshort URLs,[object Object],Twitteristhemostpopularmicrobloggingservices:1.8 mill. users in Germany (Pettey & Stevens, 2009),[object Object],190 mill. usersworldwide valuablesourceforcompanies (Pak & Paroubekl, 2010),[object Object],Free accesstothedata via Twitter API,[object Object],08/06/2011,[object Object],Sommer, Schieber / Analyzingcustomersentiments in microblogs,[object Object],3,[object Object]
Sentiment analysisisthegoal:Why do weneedtopicmodels?,[object Object],Analytics,[object Object],Sentimentanalysis,[object Object],Hugeammountofdata, only a smallsetofthedatais relevant.,[object Object],Knowledge Discovery,[object Object],in Databases,[object Object],Twitterdata,[object Object],Data source,[object Object],08/06/2011,[object Object],Sommer, Schieber / Analyzingcustomersentiments in microblogs,[object Object],4,[object Object]
Research methodology.	,[object Object],Research goals,[object Object],Identification of microblog entries containing opinions in a specific context.,[object Object],How can we automatically identify the topics of the entries?,[object Object],Research approach,[object Object],Design-science-based approach (Hevner et al., 2004),[object Object],Topic detection with generative topic models (Blei& Lafferty, 2009),[object Object],Twitter as a textual data source,[object Object],08/06/2011,[object Object],Sommer, Schieber / Analyzingcustomersentiments in microblogs,[object Object],5,[object Object]
Topic Modelling.,[object Object],„Probabilistic models for uncovering the underlying semantic structure of a document collection“ (Blei& Lafferty, 2009),[object Object],Exploratory approach:Latent Dirichlet Allocation (LDA),[object Object],Lack of knowledge about underlying correlations between topics,[object Object],LDA is allowing the documents to have a mixture of topics,[object Object],LDA is used for exploring and representing topics in microblog entries,[object Object],08/06/2011,[object Object],Sommer, Schieber / Analyzingcustomersentiments in microblogs,[object Object],6,[object Object]
Knowledge-Discovery-in-Databasesfor Twitter data sets.	,[object Object],Topic modelling,[object Object],Topic identification by implementing LDA,[object Object],Lexicalization and co-occurrences,[object Object],Transformation,[object Object],Preprocessing,[object Object],Removal of unwanted characters,[object Object],Sentiment analysis,[object Object],based on specifictopicclusters,[object Object],Selection by keywords,[object Object],Twitter search by using Twitter API,[object Object],Twitterdata,[object Object],08/06/2011,[object Object],Sommer, Schieber / Analyzingcustomersentiments in microblogs,[object Object],7,[object Object]
Data set 1: November 2010, 1.500tweets.Data set 2: January 2011, 1.200 tweets.,[object Object],Target twitter data ,[object Object],without search terms,[object Object],Search items,[object Object],3D,[object Object],Preprocessed,[object Object],targettwitterdata,[object Object],Sony 3D,[object Object],Sony 3D KDL,[object Object],08/06/2011,[object Object],Sommer, Schieber / Analyzingcustomersentiments in microblogs,[object Object],8,[object Object]
Results of the first period.November 2010.,[object Object],Identification of 10 topic clusters,[object Object],Most frequent topic “X7”,[object Object],Top 8 words which are representing topic “X7”,[object Object],Sample Twitter documents corresponding to topic “X7”,[object Object],Reduction of raw Twitter data to tweets containing words of a specific context,[object Object],Sentiment analysis of these tweets,[object Object],08/06/2011,[object Object],Sommer, Schieber / Analyzingcustomersentiments in microblogs,[object Object],9,[object Object]
Results of the second period.January 2011.,[object Object],Identification of 10 topic clusters,[object Object],Most frequent topic “X9”,[object Object],Top 5 words which are representing topic “X9”,[object Object],Tweets correspond to one topic or at least to one dominant topic ,[object Object],We separated tweets with specific topics by using generative topic models,[object Object],We obtained topics referencing current events in our short-time datasets,[object Object],The algorithm automatically detected several topics  (manual setting of topic ammount),[object Object],The topic distribution becomes more specific by specifying the search string,[object Object],08/06/2011,[object Object],Sommer, Schieber / Analyzingcustomersentiments in microblogs,[object Object],10,[object Object]
Summary and Q&A.,[object Object],Topic modellingworks in thefieldoftwitterdataanalysis.,[object Object],Wecandistinguishbetween relevant and non-relevant tweetsforspecificquestions(bythecompanies).,[object Object],A firststep in ordertoknowwhattheconversationofcustomersisreallyabout.,[object Object],Goal: Identification of microblog entries containing sentiments in a specific context.,[object Object],Limited datasets, noevaluationmetricsandtheresearchisatthebeginning.,[object Object],Developing a crawlerforlong-term analysis (large scaleevaluation).,[object Object],Evaluation ofothertopicmodelapproaches (correlated, dynamic).,[object Object],Literaturereviewanddiscussion on non topicmodelapproaches.,[object Object],08/06/2011,[object Object],Sommer, Schieber / Analyzingcustomersentiments in microblogs,[object Object],11,[object Object]
Contactdetails.,[object Object],Stefan SommerT-Systems Multimedia Solutions GmbH,[object Object],E-Mail:	s.sommer@t-systems.comPhone:	+49 170 2236 469Twitter:	@somerus,[object Object],Andreas SchieberDresden University of Technology,[object Object],E-Mail:	andreas.schieber@tu-dresden.deTelefon:	+49 351 463 32735Twitter:	@reakahont,[object Object],Slides:	http://www.slideshare.net/somerus,[object Object],08/06/2011,[object Object],Sommer, Schieber / Analyzingcustomersentiments in microblogs,[object Object],12,[object Object]
References.,[object Object],Barnes, S.J. and Böhringer, M. (2009) 'Continuance Usage Intention in Microblogging Services: The Case of Twitter', Proceedings of the 17th European Conference on Information Systems, 1-13.,[object Object],Bermingham, A. and Smeaton, A. (2010) 'Classifying Sentiment in Microblogs - Is Brevity an Advantage?', Proceedings of the 19th ACM international conference on Information and knowledge management, 1833-1836.,[object Object],Blei, D. and Lafferty, J. (2009) Topic Models, [Online], Available: http://www.cs.princeton.edu/~blei/papers/BleiLafferty2009.pdf [30 Nov 2010].,[object Object],Blei, D., Ng, A. and Jordan, M. (2003) 'Latent Dirichlet Allocation', Journal of Machine Learning Research, pp. 933-1022.,[object Object],Böhringer, M. andGluchowski, P. (2009) 'Microblogging', Informatik-Spektrum, pp. 505-510.,[object Object],Fayyad, U. (1996) Advances in Knowledge Discovery and Data Mining, Menlo Park: AAAI Press.,[object Object],Hevner, A., March, S., Park, J. and Ram, S. (2004) 'Design Science in Information Systems', MIS Quarterly, 28, pp. 75-105.,[object Object],Liu, B. (2007) Web Data Mining, Berlin: Springer.,[object Object],O'Connor, B., Balasubramanyan, R., Routledge, B. and Smith, N. (2010) 'From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series', Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, 122-129.,[object Object],Oulasvirta, A., Lehtonen, E., Kurvinen, E. and Raento, M. (2010) 'Making the ordinary visible in microblogs', Personal and ubiquitous computing, Vol. 14 (3), pp. 237-249.,[object Object],Pak, A. and Paroubek, P. (2010) 'Twitter as a Corpus for Sentiment Analysis and Opinion Mining', Proceedings of the International Conference on Language Resources and Evaluation, 1320-1326.,[object Object],Pettey, C. and Stevens, H. (2009) Gartner's Hype Cycle Special Report for 2009, [Online], Available: http://www.gartner.com/it/page.jsp?id=1124212 [7 Dec 2010].,[object Object],Ramage, D., Dumais, S. and Liebling, D. (2010) 'Characterizing Microblogs with Topic Models', Fourth International AAAI Conference on Weblogs and Social Media.,[object Object],Richter, A., Koch, M. and Krisch, J. (2007) 'Social Commerce - Eine Analyse des Wandels im E-Commerce', Bericht 2007/03, Fakultät Informaitk, Universität der Bundeswehr München.,[object Object],Stephen, A.T. and Toubia, O. (2010) 'Deriving Value from Social Commerce Networks', Journal of Marketing Research, Nr. 2 Vol. 67, pp. 215-228.,[object Object],Tumasjan, A., Sprenger, T., Sandner, P. and Welpe, I. (2010) 'Predicting Elections with Twitter - What 140 Characters Reveal about Political Sentiment', Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, 178-185.,[object Object],Twitter.com (2011) 'Your world, more connected', [Online], Available: http://blog.twitter.com/2011/08/your-world-more-connected.html [2 Aug 2011],[object Object],08/06/2011,[object Object],Sommer, Schieber / Analyzingcustomersentiments in microblogs,[object Object],13,[object Object]

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Analyzing customer sentiments in microblogs

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