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Aspect identification and ratings inference for hotel reviews

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Abstract

Today, a large volume of hotel reviews is available on many websites, such as TripAdvisor (http://www.tripadvisor.com) and Orbitz (http://www.orbitz.com). A typical review contains an overall rating, several aspect ratings, and review text. The rating is an abstract of review in terms of numerical points. The task of aspect-based opinion summarization is to extract aspect-specific opinions hidden in the reviews which do not have aspect ratings, so that users can quickly digest them without actually reading through them. The task consists of aspect identification and aspect rating inference. Most existing studies cannot utilize aspect ratings which become increasingly abundant on review hosts. In this paper, we propose two topic models which explicitly model aspect ratings as observed variables to improve the performance of aspect rating inference on unrated reviews. The experiment results show that our approaches outperform the existing methods on the data set crawled from TripAdvisor website.

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  1. http://en.wikipedia.org/wiki/RMSE

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Acknowledgment

The work is partially supported by National Science Foundation under grants CNS-1126619, IIS-121302, and CNS-1461926 and the U.S. Department of Homeland Security under grant Award Number 2010-ST-062-000039, the U.S. Department of Homeland Security’s VACCINE Center under Award Number 2009-ST-061-CI0001.

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Authors and Affiliations

  1. School of Computing and Information Sciences, Florida International University, Miami, FL, USA

    Wei Xue, Tao Li & Naphtali Rishe

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  1. Wei Xue
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  2. Tao Li
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  3. Naphtali Rishe
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Correspondence to Wei Xue.

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Xue, W., Li, T. & Rishe, N. Aspect identification and ratings inference for hotel reviews. World Wide Web 20, 23–37 (2017). https://doi.org/10.1007/s11280-016-0398-9

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