Abstract
The tremendous popularity of web-based social media is attracting the attention of the industry to take profit from the massive availability of sentiment data, which is considered of high value for Business Intelligence (BI). So far, BI has been mainly concerned with corporate data with little or null attention with the external world. However, for BI analysts, taking into account the Voice of the Customer (VoC) and the Voice of the Market (VoM) is crucial for putting in context the results of their analyses. Recent advances in Opinion Mining and Sentiment Analysis have made possible to effectively extract and summarize sentiment data from these massive social media. As a consequence, VoC and VoM can be now listened from web-based social media (e.g., blogs, reviews forums, social networks, and so on). However, new challenges arise when attempting to integrate traditional corporate data and external sentiment data. This paper aims to introduce these issues and to devise potential solutions for the near future. More specifically, the paper will focus on the proposal of a semantic data infrastructure for BI aimed at providing new opportunities for integrating traditional and social BI.
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References
Cambria, E., Song, Y., Wang, H., Howard, N.: Semantic Multi-Dimensional Scaling for Open-Domain Sentiment Analysis. IEEE Intelligent Systems (2013), doi:10.1109/MIS.2012.118
Codd, E.F.: Providing OLAP to user-analysts: An IT mandate (1993)
Esuli, A., Sebastiani, F.: SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining. In: Proc. LREC 2006, pp. 417–422 (2006)
García-Moya, L., Kudama, S., Aramburu, M.J., Berlanga, R.: Storing and analysing voice of the market data in the corporate data warehouse. Information Systems Frontiers, 1–19 (2013), doi:10.1007/s10796-012-9400-y
García-Moya, L., Anaya-Sánchez, H., Berlanga, R.: A Language Model Approach for Retrieving Product Features and Opinions from Customer Reviews. IEEE Intelligent Systems (2013), doi:10.1109/MIS.2013.37
Heath, T., Bizer, C.: Linked Data: Evolving the Web into a Global Data Space, 1st edn. Morgan & Claypool, San Rafael (2011)
Inmon, W.H.: Building the Data Warehouse. John Wiley & Sons (2005)
Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers (2012)
Lu, Y., Castellanos, M., Dayal, U., Zhai, C.X.: Automatic construction of a context-aware sentiment lexicon: an optimization approach. In: WWW 2011, pp. 347–356 (2011)
Pérez, J.M., Berlanga, R., Aramburu, M.J., Pedersen, T.B.: Contextualizing data warehouses with documents. Decision Support Systems 45(1), 77–94 (2008)
Reidenbach, R.E.: Listening to the Voice of the Market: How to Increase Market Share and Satisfy Current Customers. CRC Press (2009)
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proc. of LREC, vol. 2010 (2010)
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. JASIST 61(12), 2544–2558 (2010)
Westerski, A., Iglesias, C.A.: Exploiting Structured Linked Data in Enterprise Knowledge Management Systems. An Idea Management Case Study. In: Proc. EDOCW, pp. 395–403 (2011)
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Berlanga, R., Aramburu, M.J., Llidó, D.M., García-Moya, L. (2014). Towards a Semantic Data Infrastructure for Social Business Intelligence. In: Catania, B., et al. New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-01863-8_34
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DOI: https://doi.org/10.1007/978-3-319-01863-8_34
Publisher Name: Springer, Cham
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