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Intelligent Semantic Search Engines for Opinion and Sentiment Mining

Intelligent Semantic Search Engines for Opinion and Sentiment Mining
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Author(s): Mona Sleem-Amer (Pertimm, France), Ivan Bigorgne (Lutin, France), Stéphanie Brizard (Arisem, France), Leeley Daio Pires Dos Santos (EDF, France), Yacine El Bouhairi (Thales, France), Bénédicte Goujon (Thales, France), Stéphane Lorin (Thales, France), Claude Martineau (LIGM, France), Loïs Rigouste (Pertimm, France)and Lidia Varga (LIGM, France)
Copyright: 2012
Pages: 25
Source title: Next Generation Search Engines: Advanced Models for Information Retrieval
Source Author(s)/Editor(s): Christophe Jouis (Universite Paris III, France and LIP6-Universite Pierre et Marie Curie, France), Ismail Biskri (Universite du Quebec A Trois Rivieres, Canada), Jean-Gabriel Ganascia (LIP6 and CNRS-Universite Pierre et Marie Curie, France)and Magali Roux (LIP6 and CNRS-Universite Pierre et Marie Curie, France)
DOI: 10.4018/978-1-4666-0330-1.ch009

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Abstract

Over the last years, research and industry players have become increasingly interested in analyzing opinions and sentiments expressed on the social media web for product marketing and business intelligence. In order to adapt to this need search engines not only have to be able to retrieve lists of documents but to directly access, analyze, and interpret topics and opinions. This article covers an intermediate phase of the ongoing industrial research project ’DoXa’ aiming at developing a semantic opinion and sentiment mining search engine for the French language. The DoXa search engine enables topic related opinion and sentiment extraction beyond positive and negative polarity using rich linguistic resources. Centering the work on two distinct business use cases, the authors analyze both unstructured Web 2.0 contents (e.g., blogs and forums) and structured questionnaire data sets. The focus is on discovering hidden patterns in the data. To this end, the authors present work in progress on opinion topic relation extraction and visual analytics, linguistic resource construction as well as the combination of OLAP technology with semantic search.

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