research-article

A large-scale sentiment analysis for Yahoo! answers

Authors:

Onur Kucuktunc,

B. Barla Cambazoglu,

Hakan FerhatosmanogluAuthors Info & Claims

WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining

Pages 633 - 642

https://doi.org/10.1145/2124295.2124371

Published: 08 February 2012 Publication History

Abstract

Sentiment extraction from online web documents has recently been an active research topic due to its potential use in commercial applications. By sentiment analysis, we refer to the problem of assigning a quantitative positive/negative mood to a short bit of text. Most studies in this area are limited to the identification of sentiments and do not investigate the interplay between sentiments and other factors. In this work, we use a sentiment extraction tool to investigate the influence of factors such as gender, age, education level, the topic at hand, or even the time of the day on sentiments in the context of a large online question answering site. We start our analysis by looking at direct correlations, e.g., we observe more positive sentiments on weekends, very neutral ones in the Science & Mathematics topic, a trend for younger people to express stronger sentiments, or people in military bases to ask the most neutral questions. We then extend this basic analysis by investigating how properties of the (asker, answerer) pair affect the sentiment present in the answer. Among other things, we observe a dependence on the pairing of some inferred attributes estimated by a user's ZIP code. We also show that the best answers differ in their sentiments from other answers, e.g., in the Business & Finance topic, best answers tend to have a more neutral sentiment than other answers. Finally, we report results for the task of predicting the attitude that a question will provoke in answers. We believe that understanding factors influencing the mood of users is not only interesting from a sociological point of view, but also has applications in advertising, recommendation, and search.

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Cited By

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https://doi.org/10.1109/ACCESS.2024.3368929
Díaz OFigueroa A(2023)Improving Question Intent Identification by Exploiting Its Synergy With User AgeIEEE Access10.1109/ACCESS.2023.332245711(112044-112059)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3322457
Schwarzenberg PFigueroa A(2023)Textual Pre-Trained Models for Gender Identification Across Community Question-Answering MembersIEEE Access10.1109/ACCESS.2023.323573511(3983-3995)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3235735
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Index Terms

A large-scale sentiment analysis for Yahoo! answers
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
    2. Sociology
2. Information systems

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Published In

cover image ACM Conferences

WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining

February 2012

792 pages

ISBN:9781450307475

DOI:10.1145/2124295

General Chairs:
Eytan Adar
University of Michigan, USA
,
Jaime Teevan
Microsoft Research, USA
,
Program Chairs:
Eugene Agichtein
Emory University, USA
,
Yoelle Maarek
Yahoo! Research, Israel

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 February 2012

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WSDM'12

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WSDM'12: Fifth ACM International Conference on Web Search and Data Mining

February 8 - 12, 2012

Washington, Seattle, USA

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https://doi.org/10.1109/ACCESS.2024.3368929
Díaz OFigueroa A(2023)Improving Question Intent Identification by Exploiting Its Synergy With User AgeIEEE Access10.1109/ACCESS.2023.332245711(112044-112059)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3322457
Schwarzenberg PFigueroa A(2023)Textual Pre-Trained Models for Gender Identification Across Community Question-Answering MembersIEEE Access10.1109/ACCESS.2023.323573511(3983-3995)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3235735
Chen YZhong XLiu YDong BZheng Q(2023)A deep penetration network for sentence classificationInformation Fusion10.1016/j.inffus.2023.02.01595(174-185)Online publication date: Jul-2023
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Dubois PMaftouni MBunt A(2022)Towards More Gender-Inclusive Q&AsProceedings of the ACM on Human-Computer Interaction10.1145/35555676:CSCW2(1-23)Online publication date: 11-Nov-2022
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