An unsupervised aspect extraction strategy for monitoring real-time reviews stream. Issue 3 (May 2019)

• Record Type: Journal Article
• Authors: Dragoni, Mauro; Federici, Marco; Rexha, Andi
• Is Part Of: Information processing & management. Volume 56:Issue 3(2019:May)
• Page Start: 1103
• Page End: 1118
• Publication Date: 2019-05
• Subjects: Real-time opinion mining -- Aspect-based sentiment analysis -- Decision support system; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2018.04.010
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: One of the most important opinion mining research directions falls in the extraction of polarities referring to specific entities (aspects) contained in the analyzed texts. The detection of such aspects may be very critical especially when documents come from unknown domains. Indeed, while in some contexts it is possible to train domain-specific models for improving the effectiveness of aspects extraction algorithms, in others the most suitable solution is to apply unsupervised techniques by making such algorithms domain-independent and more efficient in a real-time environment. Moreover, an emerging need is to exploit the results of aspect-based analysis for triggering actions based on these data. This led to the necessity of providing solutions supporting both an effective analysis of user-generated content and an efficient and intuitive way of visualizing collected data. In this work, we implemented an opinion monitoring service implementing (i) a set of unsupervised strategies for aspect-based opinion mining together with (ii) a monitoring tool supporting users in visualizing analyzed data. The aspect extraction strategies are based on the use of an open information extraction strategy. The effectiveness of the platform has been tested on benchmarks provided by the SemEval campaign and have been compared with the results obtained by domain-adapted techniques.