research-article

Analyzing, Detecting, and Exploiting Sentiment in Web Queries

Authors:

Sergiu Chelaru,

Ismail Sengor Altingovde,

Stefan Siersdorfer,

Wolfgang NejdlAuthors Info & Claims

ACM Transactions on the Web (TWEB), Volume 8, Issue 1

Article No.: 6, Pages 1 - 28

https://doi.org/10.1145/2535525

Published: 01 December 2013 Publication History

Abstract

The Web contains an increasing amount of biased and opinionated documents on politics, products, and polarizing events. In this article, we present an indepth analysis of Web search queries for controversial topics, focusing on query sentiment. To this end, we conduct extensive user assessments and discriminative term analyses, as well as a sentiment analysis using the SentiWordNet thesaurus, a lexical resource containing sentiment annotations. Furthermore, in order to detect the sentiment expressed in queries, we build different classifiers based on query texts, query result titles, and snippets. We demonstrate the virtue of query sentiment detection in two different use cases. First, we define a query recommendation scenario that employs sentiment detection of results to recommend additional queries for polarized queries issued by search engine users. The second application scenario is controversial topic discovery, where query sentiment classifiers are employed to discover previously unknown topics that trigger both highly positive and negative opinions among the users of a search engine. For both use cases, the results of our evaluations on real-world data are promising and show the viability and potential of query sentiment analysis in practical scenarios.

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Index Terms

Analyzing, Detecting, and Exploiting Sentiment in Web Queries
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 8, Issue 1

December 2013

204 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/2560539

Editor:
Marc Najork
Microsoft Research

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Copyright © 2013 ACM.

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Publication History

Published: 01 December 2013

Accepted: 01 September 2013

Revised: 01 September 2013

Received: 01 September 2012

Published in TWEB Volume 8, Issue 1

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Milton APera M(2023)Into the Unknown: Exploration of Search Engines’ Responses to Users with Depression and AnxietyACM Transactions on the Web10.1145/358028317:4(1-29)Online publication date: 11-Jul-2023
https://dl.acm.org/doi/10.1145/3580283
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