A large-scale sentiment analysis for Yahoo! Answers

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dc.citation.epage642en_US
dc.citation.spage633en_US
dc.contributor.authorKüçüktunç, O.en_US
dc.contributor.authorCambazoğlu, B. B.en_US
dc.contributor.authorWeber, I.en_US
dc.contributor.authorFerhatosmanoğlu, Hakanen_US
dc.coverage.spatialSeattle, Washington, USAen_US
dc.date.accessioned2016-02-08T12:14:44Z
dc.date.available2016-02-08T12:14:44Z
dc.date.issued2012en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionConference name: Proceeding WSDM '12 Proceedings of the fifth ACM international conference on Web search and data miningen_US
dc.descriptionDate of Conference: 08 -12 February 2012en_US
dc.description.abstractSentiment 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. Copyright 2012 ACM.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T12:14:44Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2012en
dc.identifier.doi10.1145/2124295.2124371en_US
dc.identifier.isbn978-1-4503-0747-5en_US
dc.identifier.urihttp://hdl.handle.net/11693/28227
dc.language.isoEnglishen_US
dc.publisherACMen_US
dc.relation.isversionofhttps://doi.org/10.1145/2124295.2124371en_US
dc.source.titleWSDM '12 Proceedings of the fifth ACM international conference on Web search and data miningen_US
dc.subjectAttitudeen_US
dc.subjectCollaborative question answeringen_US
dc.subjectPredictionen_US
dc.subjectSentiment analysisen_US
dc.subjectSentimentalityen_US
dc.subjectCommercial applicationsen_US
dc.subjectQuestion Answeringen_US
dc.subjectResearch topicsen_US
dc.subjectWeb documenten_US
dc.subjectZip codeen_US
dc.subjectForecastingen_US
dc.subjectInformation retrievalen_US
dc.subjectWebsitesen_US
dc.subjectData miningen_US
dc.titleA large-scale sentiment analysis for Yahoo! Answersen_US
dc.typeConference Paperen_US

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