Customer Reviews Analysis Based on Information Extraction Approaches

Abstract

Part 5: Languages and OntologiesInternational audienceThe 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

Requirement mining for model-based product design

“…Recently, information retrieval, NLP, web semantics and text mining techniques have been attracting more attention from academics and industrialists who are challenged by the increasing size of textual data that needs to be collected, stored, analysed and managed in a PLM software application. Relevant research studies, including, but not limited to, Madhusudanan et al (2016), Jones et al (2016) and Zhang et al (2016) presented at the last PLM 2015 international conference -or Feldhusen et al (2012) have demonstrated the benefits of applying such bodies of knowledge to solve PLM problems. This section gives a broader literature review of the current existing solutions to not only extract textual requirements from prescriptive documents, but also classify them into disciplines.…”

Requirement mining for model-based product design

“…Relevant research studies, including, but not limited to, Madhusudanan et al (2016), Jones et al (2016) and Zhang et al (2016) presented at the last PLM 2015 international conference -or Feldhusen et al (2012) have demonstrated the benefits of applying such bodies of knowledge to solve PLM problems. This section gives a broader literature review of the current existing solutions to not only extract textual requirements from prescriptive documents, but also classify them into disciplines.…”