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@InProceedings{EPFL-CONF-163846,
   abstract    = {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 com- panies
                 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 tech- niques, how their customers
                 like their products or what is the gen- eral 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 mes- sage
                 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.},
   affiliation = {EPFL},
   author      = {Yerva, Surender Reddy and Miklós, Zoltán and Aberer, Karl},
   booktitle   = {International {C}onference on {W}eb {I}ntelligence,
                 {M}ining and {S}emantics ({WIMS}'11)},
   details     = {http://infoscience.epfl.ch/record/163846},
   documenturl = {http://infoscience.epfl.ch/record/163846/files/29-yerva-wims2011.pdf},
   isbn        = {978-1-4503-0148-0/11/05},
   keywords    = {Twitter,Company Entity, Entity Resolution,
                 Classification, Planet Data, MICS},
   location    = {Sogndal, Norway},
   oai-id      = {oai:infoscience.epfl.ch:163846},
   oai-set     = {conf},
   review      = {REVIEWED},
   status      = {PUBLISHED},
   submitter   = {169837},
   title       = {What have fruits to do with technology? The case of
                 {O}range, {B}lackberry and {A}pple.},
   unit        = {LSIR},
   year        = 2011
}