A large-scale sentiment analysis for Yahoo! Answers
Author
Küçüktunç, O.
Cambazoğlu, B. B.
Weber, I.
Ferhatosmanoğlu, Hakan
Date
2012Source 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 PaperItem Usage Stats
152
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401
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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
AttitudeCollaborative question answering
Prediction
Sentiment analysis
Sentimentality
Commercial applications
Question Answering
Research topics
Web document
Zip code
Forecasting
Information retrieval
Websites
Data mining