On the difficulty of clustering microblog texts for online reputation management

Authors:
Fernando Perez-Tellez;David Pinto;John Cardiff;Paolo Rosso
Affiliations:
SMRG, Institute of Technology, Tallaght Dublin, Ireland;FCC, Benemérita Universidad, Autónoma de Puebla, Mexico;SMRG, Institute of Technology, Tallaght Dublin, Ireland;NLE Lab. -ELiRF, Universidad, Politécnica de Valencia, Spain
Venue:
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
Year:
2011

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Abstract

In recent years microblogs have taken on an important role in the marketing sphere, in which they have been used for sharing opinions and/or experiences about a product or service. Companies and researchers have become interested in analysing the content generated over the most popular of these, the Twitter platform, to harvest information critical for their online reputation management (ORM). Critical to this task is the efficient and accurate identification of tweets which refer to a company distinguishing them from those which do not. The aim of this work is to present and compare two different approaches to achieve this. The obtained results are promising while at the same time highlighting the difficulty of this task.