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An Approach to Subjectivity Detection on Twitter Using the Structured Information

  • Juan SixtoEmail author
  • Aitor Almeida
  • Diego López-de-Ipiña
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9875)

Abstract

In this paper, we propose an approach to the subjectivity detection on Twitter micro texts that explores the uses of the structured information of the social network framework. The sentiment analysis on Twitter has been usually performed through the automatic processing of the texts. However, the established limit of 140 characters and the particular characteristics of the texts reduce drastically the accuracy of Natural Language Processing (NLP) techniques. Under these circumstances, it becomes necessary to study new data sources that allow us to extract new useful knowledge to represent and classify the texts. The structured information, also called meta-information or meta-data, provide us with alternative features of the texts that can improve the classification tasks. In this study we have analysed the use of features extracted from the structured information in the subjectivity detection task, as a first step of the polarity detection task, and their integration with classical features.

Keywords

Twitter Text categorization Data mining for social networks Subjectivity detection Social networks 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Juan Sixto
    • 1
    Email author
  • Aitor Almeida
    • 1
  • Diego López-de-Ipiña
    • 1
  1. 1.DeustoTech-Deusto Institute of TechnologyUniversidad de DeustoBilbaoSpain

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