Skip to main content
SpringerLink
Log in

SEMO: a framework for customer social networks analysis based on semantics

  • Research Article
  • Published:
Journal of Information Technology

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

References

  • 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 

  • Bagozzi, R.P., Gopinath, M. and Nyer, P.U. (1999). The Role of Emotions in Marketing, Journal of the Academy of Marketing Science 27 (2): 184–206.

    Article  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).

  • Berners-Lee, T., Hendler, J. and Lassila, O. (2001). The Semantic Web, Scientific American 284 (5): 35–40.

    Article  Google Scholar 

  • Bueren, A., Schierholz, R., Kolbe, L.N. and Brenner, W. (2005). Improving Performance of Customer-Processes with Knowledge Management, Business Process Management Journal 11 (5): 573–588.

    Article  Google Scholar 

  • Chang, C.W., Lin, C.T. and Wang, L.Q. (2009). Mining the Text Information to Optimizing the Customer Relationship Management, Expert Systems with Applications 36 (2) part 1: 1433–1443.

    Article  Google Scholar 

  • Chen, I.J. and Popovich, K. (2003). Understanding Customer Relationship Management (CRM), Business Process Management Journal 9 (5): 672–688.

    Article  Google Scholar 

  • Cleverdon, C.W., Mills, J. and Keen, E.M. (1966). Factors Determining the Performance of Indexing Systems, Cranfield, UK: College of Aeronautics.

    Google Scholar 

  • Colomo-Palacios, R., Gómez-Berbís, J.M., García-Crespo, A. and Puebla-Sánchez, I. (2008). Social Global Repository: Using semantics and social web in software projects, International Journal of Knowledge and Learning 4 (5): 452–464.

    Article  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.

  • 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 

  • Deerwester, S., Dumais, S.T., Landauer, T.K., Furnas, G.W. and Harshman, R.A. (1990). Indexing by Latent Semantic Analysis, Journal of the Society for Information Science 41 (6): 391–407.

    Article  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 

  • Eikelmann, S., Hajj, J. and Peterson, M. (2008). Opinion Piece: Web 2.0: Profiting from the threat, Journal of Direct, Data and Digital Marketing Practice 9 (3): 293–295.

    Article  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.

  • 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 

  • Gountas, J. and Gountas, S. (2007). Personality Orientations, Emotional States, Customer Satisfaction, and Intention to Repurchase, Journal of Business Research 60 (1): 72–75.

    Article  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 

  • Huang, M.H. (2001). The Theory of Emotions in Marketing, Journal of Business and Psychology 16 (2): 239–247.

    Article  Google Scholar 

  • Izard, C.E. (1977). Human Emotions, New York: Plenum Press.

    Book  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 

  • Kazmer, M.M., Burnett, G. and Dickey, M.H. (2007). Identity in Customer Service Chat Interaction: Implications for virtual reference, Library and Information Science Research 29 (1): 5–29.

    Article  Google Scholar 

  • King, S. and Burgess, T.F. (2008). Understanding Success and Failure in Customer Relationship Management, Industrial Marketing Management 37 (4): 421–431.

    Article  Google Scholar 

  • Laros, F.J.M. and Steenkamp, J.B.E.M. (2005). Emotions in Consumer Behavior: A hierarchical approach, Journal of Business Research 58 (10): 1437–1445.

    Article  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.

  • 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.

  • Mahdavi, I., Cho, N., Shirazi, B. and Sahebjamnia, N. (2008). Designing Evolving User Profile in e-CRM with Dynamic Clustering of Web Documents, Data & Knowledge Engineering 65 (2): 355–372.

    Article  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.

  • 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).

  • Miao, Q., Li, Q. and Dai, R. (2009). AMAZING: A sentiment mining and retrieval system, Expert Systems with Applications 36 (3): 7192–7198.

    Article  Google Scholar 

  • Morrison, P.J. (2008). Tagging and Searching: Search retrieval effectiveness of folksonomies on the World Wide Web, Information Processing and Management 44 (4): 1562–1579.

    Article  Google Scholar 

  • 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.

  • Salton, G., Wong, A. and Yang, C.S. (1975). A Vector Space Model for Automatic Indexing, Communications of the ACM 18 (11): 613–620.

    Article  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.

  • Schoefer, K. and Diamantopoulos, A. (2008). Measuring Experienced Emotions during Service Recovery Encounters: Construction and assessment of the ESRE scale, Service Business 2 (1): 65–81.

    Article  Google Scholar 

  • Shadbolt, N., Hall, W. and Berners-Lee, T. (2006). The Semantic Web Revisited, IEEE Intelligent Systems 21 (3): 96–101.

    Article  Google Scholar 

  • Shani, D. and Chalasani, S. (1992). Exploiting Niches Using Relationship Marketing, The Journal of Consumer Marketing 9 (3): 33–42.

    Article  Google Scholar 

  • Stone, M. (2009). Staying Customer-Focused and Trusted: Web 2.0 and customer 2.0 in financial services, The Journal of Database Marketing & Customer Strategy Management 16 (2): 101–131.

    Article  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.

  • Takashi and Manabu (2006). A Survey of Sentiment Analysis, Journal of Natural Language Processing 13 (3): 201–241.

    Article  Google Scholar 

  • Tan, S. and Zhang, J. (2008). An Empirical Study of Sentiment Analysis for Chinese Documents, Expert Systems with Applications 34 (4): 2622–2629.

    Article  Google Scholar 

  • van Atteveldt, W., Kleinnijenhuis, J., Ruigrok, N. and Stefan Schlobach, S. (2008). Good News or Bad News? Conducting Sentiment Analysis on Dutch Text to Distinguish Between Positive and Negative Relations, Journal of Information Technology & Politics 5 (1): 73–94.

    Article  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.

  • Van Dolen, W., de Ruyter, K. and Lemmink, J. (2004). An Empirical Assessment of the Influence of Customer Emotions and Contact Employee Performance on Encounter and Relationship Satisfaction, Journal of Business Research 57 (4): 437–444.

    Article  Google Scholar 

  • Van Rijsbergen, C.J. (1979). Information Retrieval, Newton, MA: Butterworth-Heinemann.

    Google Scholar 

  • Zeelenberg, M. and Pieters, R. (2004). Beyond Valence in Customer Dissatisfaction: A review and new findings on behavioral responses to regret and disappointment in failed services, Journal of Business Research 57 (4): 445–455.

    Article  Google Scholar 

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

  1. Computer Science Department, Universidad Carlos III de Madrid, Madrid, Spain

    Ángel García-Crespo, Ricardo Colomo-Palacios, Juan Miguel Gómez-Berbís & Belén Ruiz-Mezcua

Authors
  1. Ángel García-Crespo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ricardo Colomo-Palacios
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juan Miguel Gómez-Berbís
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Belén Ruiz-Mezcua
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ricardo Colomo-Palacios.

Rights and permissions

Reprints and permissions

About this article

Cite this article

García-Crespo, Á., Colomo-Palacios, R., Gómez-Berbís, J. et al. SEMO: a framework for customer social networks analysis based on semantics. J Inf Technol 25, 178–188 (2010). https://doi.org/10.1057/jit.2010.1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/jit.2010.1

Keywords

Search

Navigation