=Paper=
{{Paper
|id=Vol-1176/CLEF2010wn-WePS-TsagkiasEt2010
|storemode=property
|title=The University of Amsterdam at WePS3
|pdfUrl=https://ceur-ws.org/Vol-1176/CLEF2010wn-WePS-TsagkiasEt2010.pdf
|volume=Vol-1176
|dblpUrl=https://dblp.org/rec/conf/clef/TsagkiasB10
}}
==The University of Amsterdam at WePS3==
<pdf width="1500px">https://ceur-ws.org/Vol-1176/CLEF2010wn-WePS-TsagkiasEt2010.pdf</pdf>
<pre>
            The University of Amsterdam at WePS3

                            Manos Tsagkias, Krisztian Balog

                     ISLA, University of Amsterdam, The Netherlands
                      e.tsagkias@uva.nl, k.balog@uva.nl



      Abstract. In this paper we describe our participation in the Third Web People
      Search (WePS3) evaluation campaign. We took part in the Online Reputation
      Management (ORM) task. Ambiguity of organization names (e.g., “Amazon” or
      “Apple”) raises obvious difficulties for systems that attempt to trace mentions of
      and opinions about a specific company in Web data, in an unsupervised manner.
      Problems are further amplified in the context of user generated content, where
      proper capitalization of named entities is often absent. The ORM task, introduced
      this year, addresses this very problem, by setting out the following challenge:
      given a set of Twitter entries containing an (ambiguous) company name and given
      the homepage of the company, discriminate entries that do not refer the company.
      Given the above definition, it is natural to formulate the problem as a binary
      classification task. Our focus was on building a general organization classifier
      that predicts, for each tweet, whether it is about a company. Our goal is to assess
      how a system without external aid from other sources (the company’s homepage,
      Wikipedia entry, etc.) can perform. We, therefore, focus on extracting features
      that are organization-independent and build on the characteristics of Twitter, such
      as noisy text, abbreviations and Twitter-specific language.
      Specifically, we trained a J48 decision tree classifier using the following groups
      of features: (i) company name (matching based on character 3-grams), (ii) content
      value (whether the tweet contains URLs, hashtags or is part of a conversation),
      (iii) content quality (ratio of punctuation and capital characters), (iv) organiza-
      tional context (ratio of words found in tweets labelled as positive).
      We submitted a single run that performed around the median of all submitted
      systems. One interesting observation that requires further investigation is that our
      F-score for the negative class was substantially higher than for the positive class
      (0.55 vs. 0.36); for other teams it was usually the other way around.
      In future work we plan to build company-specific models by exploiting content
      both from Twitter and from external sources.


Acknowledgements This research was supported by the Netherlands Organisation for
Scientific Research (NWO) under project number 612.061.815 and partially by the Cen-
ter for Creation, Content and Technology (CCCT).

</pre>