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OpAL: A System for Mining Opinion from Text for Business Applications

OpAL: A System for Mining Opinion from Text for Business Applications

Alexandra Balahur, Ester Boldrini, Andrés Montoyo, Patricio Martínez-Barco
Copyright: © 2012 |Pages: 31
ISBN13: 9781613500385|ISBN10: 1613500386|EISBN13: 9781613500392
DOI: 10.4018/978-1-61350-038-5.ch007
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MLA

Balahur, Alexandra, et al. "OpAL: A System for Mining Opinion from Text for Business Applications." Business Intelligence Applications and the Web: Models, Systems and Technologies, edited by Marta E. Zorrilla, et al., IGI Global, 2012, pp. 147-177. https://doi.org/10.4018/978-1-61350-038-5.ch007

APA

Balahur, A., Boldrini, E., Montoyo, A., & Martínez-Barco, P. (2012). OpAL: A System for Mining Opinion from Text for Business Applications. In M. Zorrilla, J. Mazón, Ó. Ferrández, I. Garrigós, F. Daniel, & J. Trujillo (Eds.), Business Intelligence Applications and the Web: Models, Systems and Technologies (pp. 147-177). IGI Global. https://doi.org/10.4018/978-1-61350-038-5.ch007

Chicago

Balahur, Alexandra, et al. "OpAL: A System for Mining Opinion from Text for Business Applications." In Business Intelligence Applications and the Web: Models, Systems and Technologies, edited by Marta E. Zorrilla, et al., 147-177. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-038-5.ch007

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Abstract

The past years have marked the birth and development of the Social Web, where people freely express and search for opinions on all possible topics. This phenomenon has been proven to have a great impact on many business sectors globally. Given the proven importance of the subjective data on the Web, but bearing in mind the difficulties inherent to their textual peculiarities and large volume, efficient techniques must be employed to process this data, so that it can be fully exploited to the benefit of potential users and companies. We present the OpAL system, which implements an efficient approach to mine, classify and statistically summarize opinions, grounded on the feature-based Opinion Mining paradigm. In this approach, all components are studied, implemented and optimized using different NLP techniques. Results of different in-house and competition evaluations show that the system components have a good performance and that the techniques considered are efficient. We finally complete the proposed approach by presenting a method for opinion retrieval, which is robust and multilingual. Thus, we offer an integrated solution to build a system that is able to fully respond to user needs, from the querying to the summarized output stage. Implemented at a large scale, such systems can benefit the business environment and its customers everywhere.

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