Building a Sentiment Summarizer for Local Service Reviews
Published 1 January 2008
Sasha Blair-Goldensohn, Kerry Hannan, Ryan McDonald, Tyler Neylon, George A. Reis, Jeff Reynar
Citations391
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TL;DR
This paper presents a system that summarizes the sen- timent of reviews for a local service such as a restaurant or hotel using aspect-based summarization models, where a summary is built by extracting relevant aspects of a service, such as service or value, aggregating the sentiment per aspect, and selecting aspect-relevant text.
Abstract
Online user reviews are increasingly becoming the de-facto standard for measuring the quality of electronics, restaurants, merchants, etc. The sheer volume of online reviews makes it difficult for a human to process and extract all meaningful information in order to make an educated purchase. As a result, there has been a trend toward systems that can automatically summarize opinions from a set of reviews and display them in an easy to process manner [1, 9]. In this paper, we present a system that summarizes the sentiment of reviews for a local service such as a restaurant or hotel. In particular we focus on aspect-based summarization models [8], where a summary is built by extracting relevant aspects of a service, such as service or value, aggregating the sentiment per aspect, and selecting aspect-relevant text. We describe the details of both the aspect extraction and sentiment detection modules of our system. A novel aspect of these models is that they exploit user provided labels and domain specific characteristics of service reviews to increase quality. 1.
Keywords
Computer Science
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