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      A large-scale sentiment analysis for Yahoo! Answers

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      Author
      Küçüktunç, O.
      Cambazoğlu, B. B.
      Weber, I.
      Ferhatosmanoğlu, Hakan
      Date
      2012
      Source Title
      WSDM '12 Proceedings of the fifth ACM international conference on Web search and data mining
      Publisher
      ACM
      Pages
      633 - 642
      Language
      English
      Type
      Conference Paper
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      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. Copyright 2012 ACM.
      Keywords
      Attitude
      Collaborative question answering
      Prediction
      Sentiment analysis
      Sentimentality
      Commercial applications
      Question Answering
      Research topics
      Web document
      Zip code
      Forecasting
      Information retrieval
      Websites
      Data mining
      Permalink
      http://hdl.handle.net/11693/28227
      Published Version (Please cite this version)
      https://doi.org/10.1145/2124295.2124371
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      • Department of Computer Engineering 1370
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