Extracting customer opinions associated with an aspect by using a heuristic based sentence segmentation approach

Naime F. Kayaalp, Gary R. Weckman, William A. Young Ii, David Millie and Can Celikbilek

International Journal of Business Information Systems, 2017, vol. 26, issue 2, 236-260

Abstract: Extracting opinions from textual customer reviews is one of the most challenging but crucial tasks, which has a significant effect on decision support systems. The task becomes trickier when customers mention different aspects of an entity and different opinions associated with those aspects within the same review text. In literature, there are segmentation-based approaches, which try to parse the reviews based on the features/aspects they mention and assign opinions to those segments. It is observed that the existing models can perform better with an improved underlying segmentation model. This research aims to propose an improved segmentation model, named IMAS, to fill the gap in an existing multi-aspect segmentation model (MAS). Two aspect-opinion extraction systems were developed with two different segmentation models, and they are compared based upon their overall accuracy. As a result of the comparisons, an 8% accuracy improvement is observed by using the proposed segmentation model over the legacy one.

Keywords: review mining; aspect-opinion matching; text segmentation; decision support systems. (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=86335 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:26:y:2017:i:2:p:236-260

Access Statistics for this article

More articles in International Journal of Business Information Systems from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().