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 [BibTeX] [Marc21]
Distinguishing the Popularity Between Topics: A System for Up-to-date Opinion Retrieval and Mining in the Web
Type of publication: Conference paper
Citation: Pappas_CICLING_2013
Publication status: Accepted
Booktitle: Proceedings of the 14th International Conference on Intelligent Text Processing and Computational Linguistics
Year: 2013
Publisher: ACM
Location: Samos, Greece
Organization: LNCS
URL: http://link.springer.com/conte...
Abstract: The constantly increasing amount of opinionated texts found in the Web had a significant impact in the development of sentiment analysis. So far, the majority of the comparative studies in this field focus on analyzing fixed (offline) collections from certain domains, genres, or topics. In this paper, we present an online system for opinion mining and retrieval that is able to discover up-to-date web pages on given topics using focused crawling agents, extract opinionated textual parts from web pages, and estimate their polarity using opinion mining agents. The evaluation of the system on real-world case studies, demonstrates that is appropriate for opinion comparison between topics, since it provides useful indications on the popularity based on a relatively small amount of web pages. Moreover, it can produce genre-aware results of opinion retrieval, a valuable option for decision-makers.
Projects Idiap
Authors Pappas, Nikolaos
Katsimpras, Georgios
Stamatatos, Efstathios
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  • Pappas_CICLING_2013.pdf