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ECML '98 Proceedings of the 10th European Conference on Machine Learning
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PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
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WWW '05 Proceedings of the 14th international conference on World Wide Web
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
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Twitter is a micro-blogging service on the Web, where people can enter short messages, which then become visible to other users of the service. While the topics of these messages varies, there are a lot of messages where the users express their opinions about companies or products. Since the twitter service is very popular, the messages form a rich source of information for companies. They can learn with the help of data mining and sentiment analysis techniques, how their customers like their products or what is the general perception of the company. There is however a great obstacle for analyzing the data directly: as the company names are often ambiguous, one needs first to identify, which messages are related to the company. In this paper we address this question. We present various techniques to classify tweet messages, whether they are related to a given company or not, for example, whether a message containing the keyword "apple" is about the company Apple Inc.. We present simple techniques, which make use of company profiles, which we created semi-automatically from external Web sources. Our advanced techniques take ambiguity estimations into account and also automatically extend the company profiles from the twitter stream itself. We demonstrate the effectiveness of our methods through an extensive set of experiments.