Measures of bias in news and social media coverage are essential when investigating trends and differing perceptions of various interest groups. A significant portion of news and social media coverage contains opinions with clear economic relevance – customer and travel reviews, for example, or the articles of well-known and respected bloggers who influence purchase decisions. Analyzing and acting upon user-generated content is becoming imperative for decision makers who aim to engage large user communities.
The ever increasing amount of articles and the limits of human cognition require automated approaches to analyzing the sentiment expressed in user-generated content. As part of opinion mining, sentiment detection identifies and aggregates polar opinions – i.e., positive or negative statements about facts. For achieving accurate results, one needs to deal with the inherent ambiguities of human languages. webLyzard’s method to determine sentiment automatically has continually been optimized since 2003, directing particular attention to the context of opinionated terms when resolving such ambiguities.
webLyzard not only uses sentiment information to enrich visualizations such as tag clouds, geographic maps and information landscapes, but also offers high-performance data services for tagging third-party content.
Selected Publications
- Gindl, S., Weichselbraun, A. and Scharl, A. (2010). “Cross-Domain Contextualization of Sentiment Lexicons”, 19th European Conference on Artificial Intelligence (ECAI-2010). H. Coelho et al. Lisbon, Portugal: IOS Press: 771-776.
- Scharl, A. and Weichselbraun, A. (2008): “An Automated Approach to Investigating the Online Media Coverage of US Presidential Elections”, Journal of Information Technology & Politics, 5(1): 121-132.
- Scharl, A., Pollach, I. and Bauer, C. (2003). “Determining the Semantic Orientation of Web-based Corpora”, Intelligent Data Engineering and Automated Learning, 4th International Conference (IDEAL-2003), Hong Kong (Lecture Notes in Computer Science, vol. 2690). Ed. J. Liu et al. Berlin: Springer. 840-849.
Measures of bias in news and social media coverage are essential when investigating trends and differing perceptions of various interest groups. A significant portion of news and social media coverage contains opinions with clear economic relevance – customer and travel reviews, for example, or the articles of well-known and respected bloggers who influence purchase decisions. Analyzing and acting upon user-generated content is becoming imperative for decision makers who aim to gather feedback from very large user communities.
The ever increasing amount of articles and the limits of human cognition require automate approaches to analyzing the sentiment expressed in user-generated content. As part of opinion mining, sentiment detection identifies and aggregates polar opinions – i.e., positive or negative statements about facts. For achieving accurate results, one needs to deal with the inherent ambiguities of human languages. webLyzard’s method to determine sentiment automatically has continually been optimized since 2003, directing particular attention to the context of opinionated terms when resolving such ambiguities.
webLyzard not only embeds sentiment information (green = positive; red = negative) in various visualizations such as tag clouds, geographic maps and information landscapes, but also offers high-performance data services to tag third-party content.
Selected Publications
Weichselbraun, A., Gindl, S. and Scharl, A. (2011). Using Games with a Purpose and Bootstrapping to Create Domain-Specific Sentiment Lexicons. 20th ACM Conference on Information and Knowledge Management (CIKM-2011). Glasgow, UK: ACM : 1053-1060.
Gindl, S., Weichselbraun, A. and Scharl, A. (2010). “Cross-Domain Contextualization of Sentiment Lexicons”, 19th European Conference on Artificial Intelligence (ECAI-2010). H. Coelho et al. Lisbon, Portugal: IOS Press: 771-776.
Scharl, A. and Weichselbraun, A. (2008): “An Automated Approach to Investigating the Online Media Coverage of US Presidential Elections”, Journal of Information Technology & Politics, 5(1): 121-132.
Scharl, A., Pollach, I. and Bauer, C. (2003). “Determining the Semantic Orientation of Web-based Corpora”, Intelligent Data Engineering and Automated Learning, 4th Inter-national Conference, IDEAL-2003, Hong Kong (Lecture Notes in Computer Science, vol. 2690). Ed. J. Liu et al. Berlin: Springer. 840-849.
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