An Improved Classification Strategy for Filtering Relevant Tweets Using Bag-of-Word Classifiers

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Abstract

In this paper we have presented a classification framework for classifying tweets relevant to some specific target sectors. Due to the imposed length restriction on an individual tweet, tweet classification faces some additional challenges which are not present in most other short text classification problems, needless to say in classification of standard written text. Hence, bag-of-word classifiers, which have been successfully leveraged for text classification in other domains, fail to achieve a similar level of accuracy in classifying tweets. In this paper, we have proposed a collocation feature selection algorithm for tweet classification. Moreover, we have proposed a strategy, built on our selected collocation features, for identifying and removing confounding outliers from a training set. An Evaluation on two real world datasets shows that the proposed model yields a better accuracy than the unigram model, uni-bigram model and also a partially supervised topic model on two different classification tasks.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.21(2013) No.3 (online)DOI http://dx.doi.org/10.2197/ipsjjip.21.507------------------------------In this paper we have presented a classification framework for classifying tweets relevant to some specific target sectors. Due to the imposed length restriction on an individual tweet, tweet classification faces some additional challenges which are not present in most other short text classification problems, needless to say in classification of standard written text. Hence, bag-of-word classifiers, which have been successfully leveraged for text classification in other domains, fail to achieve a similar level of accuracy in classifying tweets. In this paper, we have proposed a collocation feature selection algorithm for tweet classification. Moreover, we have proposed a strategy, built on our selected collocation features, for identifying and removing confounding outliers from a training set. An Evaluation on two real world datasets shows that the proposed model yields a better accuracy than the unigram model, uni-bigram model and also a partially supervised topic model on two different classification tasks.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.21(2013) No.3 (online)DOI http://dx.doi.org/10.2197/ipsjjip.21.507------------------------------

Journal

  • 情報処理学会論文誌

    情報処理学会論文誌 54(6), 2013-06-15

Codes

  • NII Article ID (NAID)
    110009579907
  • NII NACSIS-CAT ID (NCID)
    AN00116647
  • Text Lang
    ENG
  • Article Type
    Journal Article
  • ISSN
    1882-7764
  • Data Source
    NII-ELS  IPSJ 
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