University of Illinois Urbana-Champaign

ReviewMiner: a novel system for multi-modal review analysis to provide visualized support for decision making

Chen, Gong

Content Files
Gong_Chen.pdf

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https://hdl.handle.net/2142/49401

Description

Title
ReviewMiner: a novel system for multi-modal review analysis to provide visualized support for decision making
Author(s)
Chen, Gong
Issue Date
2014-05-30T16:42:11Z
Director of Research (if dissertation) or Advisor (if thesis)
Zhai, ChengXiang
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2014-05-30T16:42:11Z
Keyword(s)
  • data mining
  • review analysis
Abstract
Over the past decades, a number of prominent sites for collecting and presenting reviews in different product categories have seen tremendous growth. However, the growth of number and length of the review text doesn’t provide easier access to knowledge in the review. In fact as the amount of reviews grow, people are less likely to be able to finish reading the helpful reviews. To tackle the information overload situation in review text data, I developed features on top of an existing review analyzer named ReviewMiner. It is a novel system for understanding review data. ReviewMiner is a multi-modal system that combines visualizations of spatial, temporal as well as textual summary of aspects of the review. The highly rich content produced from large amounts of review data empowers the user of the system to make more informed decisions. Because of the underlying LARA algorithm for general aspect rating analysis, ReviewMiner is a framework that can be applied to many categories of review data such as hotel review, restaurant reviews, product review, medical review, and more. In the focused study of hotel review scenario, I demonstrate the system’s major components: (1) natural language search; (2) visualizing the review data with location attributes; (3) analysis of temporal evolution of aspect rating; (4) latent aspect analysis on review text data.
Graduation Semester
2014-05
Permalink
http://hdl.handle.net/2142/49401
Copyright and License Information
Copyright 2014 Gong Chen

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