Abstract
The aim of this research is to easily monitor the reputation of a company in the Twittersphere. We propose a strategy that organizes a stream of tweets into different clusters based on the tweets’ topics. Furthermore, the obtained clusters are assigned into different priority levels. A cluster with high priority represents a topic which may affect the reputation of a company, and that consequently deserves immediate attention. The evaluation results show that our method is competitive even though the method does not make use of any external knowledge resource.
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Qureshi, M.A., O’Riordan, C., Pasi, G. (2013). Clustering with Error-Estimation for Monitoring Reputation of Companies on Twitter. In: Banchs, R.E., Silvestri, F., Liu, TY., Zhang, M., Gao, S., Lang, J. (eds) Information Retrieval Technology. AIRS 2013. Lecture Notes in Computer Science, vol 8281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45068-6_15
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DOI: https://doi.org/10.1007/978-3-642-45068-6_15
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