SEMO: a framework for customer social networks analysis based on semantics
- 150 Downloads
The increasing importance of the Internet in most domains has brought about a paradigm change in consumer relations. The influence of Social Networks has entered the Customer Relationship Management domain under the coined term CRM 2.0. In this context, the need to understand and classify the interactions of customers by means of new platforms has emerged as a challenge for both researchers and professionals worldwide. This is the perfect scenario for the use of SEMO, a platform for Customer Social Networks Analysis based on Semantics and emotion mining. The platform benefits from both semantic annotation and classification and text analysis, relying on techniques from the Natural Language Processing domain. The results of the evaluation of the experimental implementation of SEMO reveal a promising and viable platform from a technical perspective.
Keywordssocial networks customer relationship management semantics emotions natural language processing
This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the EUREKA project SITIO (TSI-020400-2009-148), SONAR2 (TSI-020100-2008-665) and GO2 (TSI-020400-2009-127).
- Baader, F., Calvanese, D., Mcguinness, D.L., Nardi, D. and Patel-Schneider, P.F. (2003). The Description Logic Handbook: Theory, implementation, and applications, Cambridge, UK: Cambridge University Press.Google Scholar
- Band, W. (2008). The Forrester Wave™: Enterprise CRM suites, Q3 2008, [WWW document] http://www.forrester.com/Research/Document/Excerpt/0,7211,44968,00.html (accessed 8th February 2010).
- Cleverdon, C.W., Mills, J. and Keen, E.M. (1966). Factors Determining the Performance of Indexing Systems, Cranfield, UK: College of Aeronautics.Google Scholar
- Danisman, T. and Alpkocak, A. (2008). Feeler: Emotion classification of text using vector space model, in F. Guerin, B. Löwe and W. Vasconcelos (eds). Proceedings of the AISB 2008 Convention, Communication, Interaction and Social Intelligence (Aberdeen, UK, 2008); Hove, East Sussex: The Society for the study of Artificial Intelligence and Simulation of Behaviour, 53–59.Google Scholar
- Davenport, T.H., Harris, J.G. and Kohli, A.K. (2001). How Do They Know Their Customers So Well? Sloan Management Review 42 (2): 63–73.Google Scholar
- Eckerson, W.W. (1995). Three Tier Client/Server Architecture: Achieving scalability, performance, and efficiency in client server applications, Open Information Systems 10 (1): 1–12.Google Scholar
- Fensel, D. (2002). Ontologies: A silver bullet for knowledge management and electronic commerce, Berlin/Heidelberg: Springer.Google Scholar
- Francisco, V., Gervás, P. and Peinado., F. (2007). Ontological Reasoning to Configure Emotional Voice Synthesis, in Proceedings of the First International Conference of Web Reasoning and Rule Systems (Innsbruck, Austria, 2007); Berlin/Heidelberg: Springer, 88–102.Google Scholar
- García-Crespo, A., Colomo-Palacios, R., Mencke, M. and Gómez-Berbís, J.M. (2008). CUSENT: Social sentiment analysis using semantics for customer feedback, in M.D. Lytras and P. Ordóñez (eds.) Social Web Evolution: Integrating semantic applications and web 2.0 technologies, Hershey, PA: IGI Global.Google Scholar
- Giunchiglia, F., Marchese, M. and Zaihrayeu, I. (2007). Encoding Classifications into Lightweight Ontologies, Journal on Data Semantics 8: 57–81.Google Scholar
- Heyman, P. and Garcia-Molina, H. (2006). Collaborative Creation of Communal Hierarchical Taxonomies in Social Tagging Systems, Palo Alto, Ca: Stanford University.Google Scholar
- Jarrar, M. (2008). Towards Effectiveness and Transparency in E-Business Transactions, An Ontology for Customer Complaint Management, in R. García (ed.) Semantic Web for Business: Cases and applications, Hershey, PA: IGI Global.Google Scholar
- Levitt, T. (1983). After the Sale is Over,…, Harvard Business Review 61 (5): 87–94.Google Scholar
- Linoff, G.S. and Berry, M.J. (2002). Mining the Web, Transforming Customer Data into Customer Value, New York: John Wiley.Google Scholar
- López, J.M., Gil, R., García, R., Cearreta, I. and Garay, N. (2008). Towards an Ontology for Describing Emotions, in Proceedings of the 1st World Summit on the Knowledge Society (Athens, Greece, 2008); Berlin/Heidelberg: Springer, 96–104.Google Scholar
- Magro, D. and Goy, A. (2008). The Business Knowledge for Customer Relationship Management: An ontological perspective, in Proceedings of 1st International Workshop on Ontology-supported Business Intelligence (Karlsruhe, Germany, 2008); New York, NY: ACM International Conference Proceeding Series. Article No.4.Google Scholar
- Maoz, M. (2008). Magic quadrant for CRM customer service contact centers, 2008, [WWW document] http://www.gartner.com/DisplayDocument?id=626908 (accessed 8th February 2010).
- Marston, P. (2008). The Forrester Wave™: Midmarket CRM suites, Q3 2008, [WWW document] http://www.forrester.com/Research/Document/Excerpt/0,7211,44969,00.html (accessed 8th February 2010).
- Mathieu, Y. (2005). Annotation of Emotions and Feelings in Texts, in Proceedings of the First International Conference ASCII 2005 (Beijing, China, 2005); Berlin/Heidelberg: Springer, 350–357.Google Scholar
- McKinsey (2007). How Businesses are Using Web 2.0: A McKinsey global survey, The McKinsey Quarterly, March 2007. [WWW document] http://www.mckinseyquarterly.com/PDFDownload.aspx?L2=16&L3=16&ar=1913&gp=0 (accessed 8th February 2010).
- OpenSocial (2008). Google code official web site, [WWW document] http://code.google.com/apis/opensocial/ (accessed 8th February 2010).
- O’Reilly, T. (2005). What is web 2.0. Design patterns and business models for the next generation of software, 30 September 2005 [WWW document] http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html (accessed 8th February 2010).
- O’Reilly, T. (2007). What is Web 2.0: Design patterns and business models for the next generation of software, Communications & Strategies 1: 17–27.Google Scholar
- Rivera, I., Mencke, M., Gómez, J.M., Alor-Hernández, G. and García-Crespo, A. (2008). A Collaborative Open Social Network Dataset based on Email Ranking and Filtering, in Proceedings of the 3rd IEEE International Conference on Systems (Cancún, Mexico, 2008); Washington, DC: IEEE Computer Society Press, 13–18.Google Scholar
- Schmitz, P. (2006). Inducing Ontology from Flickr Tags. Collaborative Web Tagging Workshop, in Proceedings of the 15th WWW Conference (Edinburgh, UK, 2006); New York: ACM Press, 63–72.Google Scholar
- Strapparava, C. and Mihalcea, R. (2008). Learning to Identify Emotions in Text, in Proceedings of the 2008 ACM symposium on Applied computing (Fortaleza, Ceara, Brazil); Rochester, Il: ACM Publishing, 1556–1560.Google Scholar
- Van Damme, C., Christiaens, S. and Vandijck, E. (2007). Building an Employee-Driven CRM Ontology, in Proceedings of the IADIS Multi Conference on Computer Science and Information Systems (Lisbon, Portugal, 2007); Lisbon, Portugal: IADIS Press, 330–334.Google Scholar
- Van Rijsbergen, C.J. (1979). Information Retrieval, Newton, MA: Butterworth-Heinemann.Google Scholar