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On Mining Opinions from Social Media

  • Conference paper
Engineering Applications of Neural Networks (EANN 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 383))

Abstract

The broad use of social media nowadays has led many users to express their opinions on various subjects through them. The need for these opinions to be automatically labeled and categorized according to their sentiment, has also arisen. In this paper, a novel sentiment analysis approach is introduced, which takes into account the total number of idiomatic expressions and emoticons that are used in the text, and simultaneously processes the original text in Greek along with its automatic translation in English as well. Moreover, the novelty of the proposed solution lies in the difficulty of Modern Greek language and the fact that the text in social media is mainly unstructured. The proposed methodology is tested on two distinct data sets of opinions regarding a certain matter, which have been collected from Facebook and Twitter respectively. Finally, we discuss the performance of various classiffication algorithms and we compare the extracted experimental results.

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Author information

Authors and Affiliations

  1. Department of Information and Communication Systems Engineering, University of the Aegean, 83200, Karlovassi, Greece

    Vicky Politopoulou & Manolis Maragoudakis

Authors
  1. Vicky Politopoulou
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  2. Manolis Maragoudakis
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Editor information

Editors and Affiliations

  1. Department of Forestry & Management of the Environment and Natural Resources, Democritus University of Thrace, GR-68200, Orestiada, Hellas

    Lazaros Iliadis

  2. Frederick University of Cyprus, Cyprus

    Harris Papadopoulos

  3. Faculty of Engineering and Computing, Coventry University, UK

    Chrisina Jayne

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Politopoulou, V., Maragoudakis, M. (2013). On Mining Opinions from Social Media. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_49

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  • DOI: https://doi.org/10.1007/978-3-642-41013-0_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41012-3

  • Online ISBN: 978-3-642-41013-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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