2026-09-21T17:38:39Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/49401 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Zhai, ChengXiang Chen, Gong 2014-05-30T16:42:11Z 2014-05-30T16:42:11Z 2014-05 2014-05-30T16:42:11Z 2014-05 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. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2014-05-01T13:46:40Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 3_MyThesis.docx: 2138483 bytes, checksum: 28b535b91ced70ec516330cf81a9b63e (MD5) Chen_Gong.pdf: 1048152 bytes, checksum: db90e64cc5f0fd2bba1ec201665ff8e4 (MD5) Made available in DSpace on 2014-05-30T16:42:11Z (GMT). No. of bitstreams: 3 Gong_Chen.pdf: 1048152 bytes, checksum: db90e64cc5f0fd2bba1ec201665ff8e4 (MD5) 3_MyThesis.docx: 2138483 bytes, checksum: 28b535b91ced70ec516330cf81a9b63e (MD5) license.txt: 4058 bytes, checksum: 53426c75ec43f7d1f3670377c5543c0b (MD5) http://hdl.handle.net/2142/49401 en Copyright 2014 Gong Chen data mining review analysis ReviewMiner: a novel system for multi-modal review analysis to provide visualized support for decision making text Computer Science 1434 Computer Science 0112 University of Illinois at Urbana-Champaign Thesis M.S. MS:Computer Science -UIUC 10KS0112MS