Onur Kucuktunc
B. Barla Cambazoglu
Ingmar Weber
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.
- A. Abbasi, H. Chen, and A. Salem. Sentiment analysis in multiple languages: feature selection for opinion classification in Web forums. ACM Trans. Inf. Syst., 26:12:1--12:34, 2008. Google Scholar
Digital Library
- X. Bai. Predicting consumer sentiments from online text. Decis. Support Syst., 50:732--742, 2011. Google ScholarDigital Library
- P. Beineke, T. Hastie, C. Manning, and S. Vaithyanathan. Exploring sentiment summarization. In Proc. AAAI Spring Symp. Exploring Attitude and Affect in Text: Theories and Applications, pages 1--4, 2004.Google Scholar
- J. Bollen, H. Mao, and A. Pepe. Determining the public mood state by analysis of microblogging posts. In Proc. Alife XII Conf., pages 667--668, 2010.Google Scholar
- J. Bollen, H. Mao, and X. Zeng. Twitter mood predicts the stock market. J. Comput. Sci, 2:1--8, 2011.Google ScholarCross Ref
- S. I. Calderon. Facebook shares new data on relationship status and sentiment, 2010. http://www.insidefacebook.com/2010/02/15/dr-facebook-is-in-people-in-relationships-are-happiest/.Google Scholar
- S. R. Das and M. Y. Chen. Yahoo! for Amazon: sentiment extraction from small talk on the Web. Manage. Sci., 53:1375--1388, 2007. Google ScholarDigital Library
- K. Dave, S. Lawrence, and D. M. Pennock. Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In Proc. 12th Int'l Conf. World Wide Web, pages 519--528, 2003. Google ScholarDigital Library
- A. Devitt and K. Ahmad. Sentiment analysis in financial news: a cohesion-based approach. In Proc. 45th Annual Meeting of the Assoc. for Computational Linguistics, pages 984--991, 2007.Google Scholar
- J. H. Friedman. Greedy function approximation: a gradient boosting machine. Ann. Stat., 29(5):1189--1232, 2001.Google ScholarCross Ref
- S. Gerani, M. J. Carman, and F. Crestani. Investigating learning approaches for blog post opinion retrieval. In Proc. 31th Eur. Conf. Information Retrieval, pages 313--324, 2009. Google ScholarDigital Library
- S. Gerani, M. J. Carman, and F. Crestani. Proximity-based opinion retrieval. In Proc. 33rd Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pages 403--410, 2010. Google ScholarDigital Library
- N. Godbole, M. Srinivasaiah, and S. Skiena. Large-scale sentiment analysis for news and blogs. In Proc. Int'l Conf. Weblogs and Social Media, 2007.Google Scholar
- M. L. Gregory, N. Chinchor, P. Whitney, R. Carter, E. Hetzler, and A. Turner. User-directed sentiment analysis: visualizing the affective content of documents. In Proc. Workshop on Sentiment and Subjectivity in Text, pages 23--30, 2006. Google ScholarDigital Library
- S. D. Kamvar and J. Harris. We feel fine and searching the emotional web. In Proc. 4th ACM Int'l Conf. Web Search and Data Mining, pages 117--126, 2011. Google ScholarDigital Library
- S.-M. Kim and E. H. Hovy. Crystal: analyzing predictive opinions on the Web. In Proc. 2007 Joint Conf. Empirical Methods in Natural Language and Computational Natural Language Learning, pages 1056--1064, 2006.Google Scholar
- K. Lerman, S. Blair-Goldensohn, and R. McDonald. Sentiment summarization: evaluating and learning user preferences. In Proc. 12th Conf. European Chapter of the Assoc. for Computational Linguistics, pages 514--522, 2009. Google ScholarDigital Library
- B. Pang and L. Lee. Opinion mining and sentiment analysis. Found. Trends Inf. Retr., 2:1--135, 2008. Google ScholarDigital Library
- B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up?: sentiment classification using machine learning techniques. In Proc. 2002 Conf. Empirical Methods in Natural Language Processing, pages 79--86, 2002. Google ScholarDigital Library
- M. Thelwall. Emotion homophily in social network site messages. First Monday, 15(4--5), 2010.Google Scholar
- M. Thelwall, K. Buckley, and G. Paltoglou. Sentiment in Twitter events. J. Am. Soc. Inf. Sci. Techn., 62:406--418, 2011. Google ScholarDigital Library
- M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas. Sentiment in short strength detection informal text. J. Am. Soc. Inf. Sci. Technol., 61:2544--2558, 2010. Google ScholarDigital Library
- M. Thelwall, D. Wilkinson, and S. Uppal. Data mining emotion in social network communication: gender differences in MySpace. J. Am. Soc. Inf. Sci. Technol., 61(1):190--199, 2010. Google ScholarDigital Library
- M. Thomas, B. Pang, and L. Lee. Get out the vote: determining support or opposition from congressional floor-debate transcripts. In Proc. Conf. Empirical Methods in Natural Language Processing, pages 327--335, 2006. Google ScholarDigital Library
- P. D. Turney. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In Proc. 40th Annual Meeting on Assoc. for Computational Linguistics, pages 417--424, 2002. Google ScholarDigital Library
- I. Weber and C. Castillo. The demographics of web search. In Proc. 33rd Int'l ACM SIGIR Conf. Research and Development in Information Retrieval, pages 523--530, 2010. Google ScholarDigital Library
- J. Ye, J.-H. Chow, J. Chen, and Z. Zheng. Stochastic gradient boosted distributed decision trees. In Proc. 18th ACM Conf. Information and Knowledge Management, pages 2061--2064, 2009. Google ScholarDigital Library
- J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack. Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques. In Proc. 3rd IEEE Int'l Conf. Data Mining, pages 427--434, 2003. Google ScholarDigital Library
- W. Zhang, C. Yu, and W. Meng. Opinion retrieval from blogs. In Proc. 16th ACM Conf. Information and Knowledge Management, pages 831--840, 2007. Google ScholarDigital Library
Index Terms
- A large-scale sentiment analysis for Yahoo! answers
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