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Analysis of the Structured Information for Subjectivity Detection in Twitter

  • Juan SixtoEmail author
  • Aitor Almeida
  • Diego López-de-Ipiña
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10840)

Abstract

In this paper, we analyze the opportunities of the structured information of the social networks for the subjectivity detection on Twitter micro texts. 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 when compared with other domains. 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 paper we analyze the features of the structured information and their usefulness in the opinion mining sub-domain, specially in the subjectivity detection task. Also present a novel classification of these features according to their origin.

Keywords

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

Notes

Acknowledgements

This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the project E-RMP (CSO2015-64495-R).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

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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