Customer Reviews Analysis Based on Information Extraction Approaches

Abstract : The existing information extraction approaches are generally analyzed and then categorized into several groups based on the superiority and the intelligence of the approaches as well as their capability to solve complex problems. Two practical approaches are provided to clarify how to use the information extraction solutions to obtain the valuable information from numerous reviews. The first approach is to support the front-end services in the EASY-IMP project. The customer preference and the optimum interest of customers is determined based on TF-IDF approach. Roughly 100,000 pages have been analyzed and the customer preference is studied based on the most relevant keywords. However, TF-IDF approach limits on the capability to provide the personalized infromation, which can only obtain the restricted information based on weights calcualtion. In order to extract more efficient customerized infromation, an opinion mining algorithm is proposed. The proposed algorithm aims to obtain sufficient information extraction results and reduce the complexity and running time of information extraction by jointly discovering the main opinion mining elements. The analyzed reviews show that the proposed algorithm can effectively and simultaneously identify the main elements.
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Haiqing Zhang, Aicha Sekhari, Florendia Fourli-Kartsouni, Yacine Ouzrout, Abdelaziz Bouras. Customer Reviews Analysis Based on Information Extraction Approaches. 12th IFIP International Conference on Product Lifecycle Management (PLM 2015), Oct 2015, Doha, Qatar. pp.227-237, ⟨10.1007/978-3-319-33111-9_21⟩. ⟨hal-01377446⟩

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