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Vector Space Models for the Classification of Short Messages on Social Network Services

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Web Information Systems and Technologies (WEBIST 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 189))

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Abstract

In this chapter we review vector space models to propose a new one based on the Jensen-Shannon divergence with the goal of classifying ignored short messages on a social network service. We assume that ignored messages are those published ones that were not interacted with. Our goal then is to attempt to classify messages to be published as ignored to discard them from a set messages that can be used by a recommender system. To evaluate our model, we conduct experiments comparing different models on a Twitter dataset with more than 13,000 Twitter accounts. Results show that our best model tested obtained an average accuracy of 0.77, compared to 0.74 from a model from the literature. Similarly, this method obtained an average precision of 0.74 compared to 0.58 from the second best performing model.

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Notes

  1. 1.

    http://www.tweetstats.com/graphs/nytimes/zoom/2012/Sep

  2. 2.

    These numbers are based on discussions in blog posts such as in http://thenextweb.com/twitter/2012/01/07/interesting-fact-most-tweets-posted-are -approximately-30-characters-long/ and http://www.ayman-naaman.net/2010/04/21/how-many-characters-do-you-tweet/. But they do not provide an average. In our own dataset presented in Sect. 4.1, the average number of characters in a tweet is 84.

  3. 3.

    Note that we do not normalize our tf-idf model based on message length since all tend to have similar sizes [18].

  4. 4.

    https://dev.twitter.com/docs/api/1/get/statuses/sample

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Authors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Ricardo Lage, Peter Dolog & Martin Leginus

  2. LIP6, University Pierre Et Marie Curie (Paris 6), Paris, France

    Ricardo Lage

Authors
  1. Ricardo Lage
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  2. Peter Dolog
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  3. Martin Leginus
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Corresponding author

Correspondence to Peter Dolog .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Karl-Heinz Krempels

  2. Virtual Vehicle Research Center, Graz, Austria

    Alexander Stocker

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Lage, R., Dolog, P., Leginus, M. (2014). Vector Space Models for the Classification of Short Messages on Social Network Services. In: Krempels, KH., Stocker, A. (eds) Web Information Systems and Technologies. WEBIST 2013. Lecture Notes in Business Information Processing, vol 189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44300-2_13

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  • DOI: https://doi.org/10.1007/978-3-662-44300-2_13

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