Commenced in January 2007
Paper Count: 30121
Social Media Idea Ontology: A Concept for Semantic Search of Product Ideas in Customer Knowledge through User-Centered Metrics and Natural Language Processing
Abstract:In order to survive on the market, companies must constantly develop improved and new products. These products are designed to serve the needs of their customers in the best possible way. The creation of new products is also called innovation and is primarily driven by a company’s internal research and development department. However, a new approach has been taking place for some years now, involving external knowledge in the innovation process. This approach is called open innovation and identifies customer knowledge as the most important source in the innovation process. This paper presents a concept of using social media posts as an external source to support the open innovation approach in its initial phase, the Ideation phase. For this purpose, the social media posts are semantically structured with the help of an ontology and the authors are evaluated using graph-theoretical metrics such as density. For the structuring and evaluation of relevant social media posts, we also use the findings of Natural Language Processing, e. g. Named Entity Recognition, specific dictionaries, Triple Tagger and Part-of-Speech-Tagger. The selection and evaluation of the tools used are discussed in this paper. Using our ontology and metrics to structure social media posts enables users to semantically search these posts for new product ideas and thus gain an improved insight into the external sources such as customer needs.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316746Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 366
 H. Smith, “A CSC white paper, european office of technology and innovation. what innovation is. how companies develop operating systems for innovation.” (Online). Available: http://www.innovationmanagement.se/wp-content/uploads/pdf/ innovation update 2005.pdf, last accessed on 11.11.2017.
 J. Xu, R. Houssin, E. Caillaud, and M. Gardoni, “Macro process of knowledge management for continuous innovation,” in Journal of Knowledge Management, vol. 14, pp. 573–591. (Online). Available: http://www.emeraldinsight.com/doi/abs/10.1108/13673271011059536, last accessed on 03.04.2017.
 A.-L. Mention, “Co-operation and co-opetition as open innovation practices in the service sector: Which influence on innovation novelty?” in Technovation, ser. Open Innovation - ISPIM Selected Papers, vol. 31, pp. 44–53. (Online). Available: http://www.sciencedirect.com/science/article/pii/S0166497210000908, last accessed on 03.08.2017.
 R. Alt and O. Reinhold, Social Customer Relationship Management. Springer Berlin Heidelberg. (Online). Available: http://link.springer.com/10.1007/978-3-662-52790-0, last accessed on 22.01.2017.
 H. I. Ansoff, “Managing strategic surprise by response to weak signals,” vol. 18, no. 2, pp. 21–33. (Online). Available: https://doi.org/10.2307/41164635 last accessed on 07.010.2017.
 R. Eckhoff, J. Frank, M. Markus, M. Lassnig, and S. Schoen, “Detecting innovation signals with technology-enhanced social media analysis - experiences with a hybrid approach in three branches,” vol. 17, no. 1, pp. 120–130. (Online). Available: http://www.ijisr.issr-journals.org/abstract.php?article=IJISR-15-065-09, last accessed on 07.06.2017.
 M. Markus, R. A. Eckhoff, and M. Lassnig, “Innovation signals in online-communitys ein komplementaerer analytischer ansatz,” vol. 50, no. 5, pp. 13–21. (Online). Available: http://link.springer.com/10.1007/BF03340849, last accessed on 17.11.2017.
 Apache tomcat (Online). Available: http://tomcat.apache.org/, last accessed on 17.11.2017.
 Apache marmotta. (Online). Available: http://marmotta.apache.org/, last accessed on 17.11.2017.
 Apache marmotta - KiWi triple store. (Online). Available: http://marmotta.apache.org/kiwi/, last accessed on 17.11.2017.
 SPARQL query language for RDF. (Online). Available: https://www.w3.org/TR/rdf-sparql-query/, last accessed on 17.11.2017.
 D. Thorleuchter, D. V. den Poel, and A. Prinzie, “Mining ideas from textual information,” vol. 37, no. 10, pp. 7182–7188. (Online). Available: http://linkinghub.elsevier.com/retrieve/pii/S0957417410002848, last accessed on 29.10.2017.
 D. Thorleuchter and D. Van den Poel, “Web mining based extraction of problem solution ideas,” vol. 40, no. 10, pp. 3961–3969. (Online). Available: http://linkinghub.elsevier.com/retrieve/pii/S095741741300016X, last accessed on 28.10.2017.
 A. Westerski, C. A. Iglesias, and F. T. Rico, “A model for integration and interlinking of idea management systems,” in Metadata and Semantic Research, ser. Communications in Computer and Information Science. Springer, Berlin, Heidelberg, pp. 183–194. (Online). Available: https://link.springer.com/chapter/10.1007/978-3-642-16552-8 18, last accessed on 17.10.2017.
 Idea storm. (Online). Available: http://www.ideastorm.com/, last accessed on 20.10.2017.
 M. Haeusl, J. Forster, and D. Kailer, “An approach to identify SPAM tweets based on metadata.” IEEE, pp. 48–51. (Online). Available: http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7397420, last accessed on 17.03.2017.
 Eccleston and Griseri, “How does web 2.0 stretch traditional influencing patterns? international journal of market research,” no. 50, pp. 591–661.
 NameAPI - intelligence in names. (Online). Available: https://www.nameapi.org/, last accessed on 22.10.2017.
 D. Rao, D. Yarowsky, A. Shreevats, and M. Gupta, “Classifying latent user attributes in twitter.” ACM Press, p. 37. (Online). Available: http://portal.acm.org/citation.cfm?doid=1871985.1871993, last accessed on 20.10.2017.
 D. Klein and C. D. Manning, “Accurate unlexicalized parsing,” in Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1, ser. ACL ’03. Association for Computational Linguistics, pp. 423–430. (Online). Available: https://doi.org/10.3115/1075096.1075150, last accessed on 08.11.2017.
 G. Angeli, M. Jose Johnson Premkumar, and C. D. Manning, “Leveraging linguistic structure for open domain information extraction,” vol. 1, pp. 344–354.
 L. Derczynski, A. Ritter, S. Clark, and K. Bontcheva, “Twitter part-of-speech tagging for all: Overcoming sparse and noisy data,” in International Conference Recent Advances in Natural Language Processing, RANLP.
 M. P. Marcus, M. A. Marcinkiewicz, and B. Santorini, “Building a large annotated corpus of english: The penn treebank,” vol. 19, no. 2, pp. 313–330. (Online). Available: http://dl.acm.org/citation.cfm?id=972470.972475, last accessed on 12.10.2017.
 K. Toutanova, D. Klein, C. D. Manning, and Y. Singer, “Feature-rich part-of-speech tagging with a cyclic dependency network,” in Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1, ser. NAACL ’03. Association for Computational Linguistics, pp. 173–180. (Online). Available: https://doi.org/10.3115/1073445.1073478, last accessed on 01.11.2017.
 P. P.-S. Chen, “The entity-relationship model toward a unified view of data,” vol. 1, no. 1, pp. 9–36. (Online). Available: http://doi.acm.org/10.1145/320434.320440, last accessed on 05.11.2017.
 B. T. Todorovic, S. R. Rancic, I. M. Markovic, E. H. Mulalic, and V. M. Ilic, “Named entity recognition and classification using context hidden markov model,” in 2008 9th Symposium on Neural Network Applications in Electrical Engineering, pp. 43–46.
 P. D. Turney, “Learning algorithms for keyphrase extraction,” vol. 2, no. 4, pp. 303–336. (Online). Available: http://link.springer.com/article/10.1023/A:1009976227802, last accessed on 01.11.2017.
 R. A. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., 1999.