Ad verba per numeros
Lately I'm pretty interested in sentiment analysis a.k.a. opinion mining. Obviously, I'm not the only one and there exist several applications trying to do some of that stuff over user generated content (e.g. twitrratr). Needless to say, performance is quite limited and there is still much room for improvement (which, from my point of view, are good news).A couple of weeks ago I suggested to a group of students to develop a naïve subjectivity analyzer based on the Subjectivity Lexicon compiled by Wilson, Wiebe and Hoffmann (2005). Some of them have developed pretty amazing apps including a couple of Twitrratr clones, another one to give scores to movies based on their reviews and a really inspiring tool by Diego Guerra to "detect" the users mood from the songs they listen to at last.fm.To do that he employs WordNet Affect, to be precise the subset provided for SemEval-2007, and, of course, a pinch of "magic" Here you have a couple of pie charts showing the different moods from three different last.fm users:
Have I aroused your curiosity? Great! Here you have three papers you can find of interest:
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Have I aroused your curiosity? Great! Here you have three papers you can find of interest:
- Balog, K., Mishne, G., and de Rijke, M. Why are they excited? identifying and explaining spikes in blog mood levels. Proceedings 11th Meeting of the European Chapter of the Association for Computational Linguistics (EACL 2006), April, (2006).
- Balog, K. and de Rijke, M. Decomposing bloggers moods: Towards a time series analysis of moods in the blogosphere. Proc. of the World Wide Web 2006 Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics. Edinburgh, (2006).
- Mishne, G. and de Rijke, M. Capturing global mood levels using blog posts. AAAI 2006 Spring Symposium on Computational Approaches to Analysing Weblogs, (2006).
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