The recent prominence of the real-time web is proving both challenging and disruptive for information retrieval and web data mining research. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user's query at a point in time, automated methods are required to sift through this information. Sentiment analysis offers a promising direction for modelling microblog content. We build and evaluate a sentiment-based filtering system using real-time user studies. We find a significant role played by sentiment in the search scenarios, observing detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users' prior topic sentiment. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
CITATION STYLE
Bermingham, A., & Smeaton, A. F. (2012). An evaluation of the role of sentiment in second screen microblog search tasks. In ICWSM 2012 - Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (pp. 399–402). https://doi.org/10.1609/icwsm.v6i1.14332
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