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A Two-Iteration Clustering Method to Reveal Unique and Hidden Characteristics of Items Based on Text Reviews

Published: 18 May 2015 Publication History

Abstract

This paper presents a new method for extracting unique features of items based on their textual reviews. The method is built of two similar iterations of applying a weighting scheme and then clustering the resultant set of vectors. In the first iteration, restaurants of similar food genres are grouped together into clusters. The second iteration reduces the importance of common terms in each such cluster, and highlights those that are unique to each specific restaurant. Clustering the restaurants again, now according to their unique features, reveals very interesting connections between the restaurants.

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  • (2019)Understanding and Encouraging Online Reviewing With a Selection-Based Review SystemInteracting with Computers10.1093/iwc/iwz02931:5(446-464)Online publication date: 12-Dec-2019

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cover image ACM Other conferences
WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web
May 2015
1602 pages
ISBN:9781450334730
DOI:10.1145/2740908

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  • IW3C2: International World Wide Web Conference Committee

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2015

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Author Tags

  1. clustering
  2. latent connections
  3. text mining
  4. textual reviews

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WWW '15
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  • IW3C2

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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  • (2019)Understanding and Encouraging Online Reviewing With a Selection-Based Review SystemInteracting with Computers10.1093/iwc/iwz02931:5(446-464)Online publication date: 12-Dec-2019

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