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Sensing Social Media: A Range of Approaches for Sentiment Analysis

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Cyberemotions

Part of the book series: Understanding Complex Systems ((UCS))

Abstract

Sentiment analysis deals with the computational detection and extraction of opinions, beliefs and emotions in written text. It combines theories and methodologies from a diverse set of scientific domains, such as psychology, natural language processing and machine learning. It fulfils the very important role of transforming the unstructured textual communication between social media users into quantifiable and informed estimations of expressed sentiment, which can subsequently be used by physicists, sociologists, complex system experts in studying the collective properties of such phenomena. The problem has been addressed from two different but often complementary directions: lexicon-based solutions that rely on sentiment dictionaries (i.e., lists of words in which each token is annotated with an indication of the affective content it typically conveys) and machine learning solutions that automatically or semi-automatically learn to detect the affective content of text. In this chapter, we discuss a range of solutions and their strengths and weaknesses in different environments and settings. We conclude that based on the application environment as well as the desired output, different types of analyses are appropriate, with varying levels of predictive accuracy.

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Notes

  1. 1.

    Refer to Chap. 1.5 of Pang and Lee (2008) for a detailed discussion about the terminology.

  2. 2.

    Most information retrieval systems and algorithms explicitly use this information in estimating the relevancy of a document in regard to a user query (Robertson et al. 2004).

  3. 3.

    Valence and arousal are discussed in more depth in Sect. 6.2.2.

  4. 4.

    In contrast, ‘unsupervised’ machine learning algorithms do not require annotated data.

  5. 5.

    http://www.bbc.co.uk/messageboards/.

  6. 6.

    http://www.digg.com.

  7. 7.

    The BBC message boards are closely administered and moderated, while Digg posts aren’t.

  8. 8.

    Studies (Bradley and Lang 1999) have shown that emotions can be characterised as “coincidence of values on a number of different strategic dimensions”, such as valence and arousal.

  9. 9.

    http://www.livejournal.com.

  10. 10.

    Stratification ensures that the splits for training and testing are as equal as possible for every class.

  11. 11.

    http://www.twitter.com.

  12. 12.

    The extraction took place in 2009, before the website changed its focus to promotion of music.

  13. 13.

    All the Weka .arff files for all datasets are available upon request.

  14. 14.

    The full list of discussion threads can be found in the Appendix material as http://doi.ieeecomputersociety.org/10.1109/T-AFFC.2012.26.

  15. 15.

    That may be due to the fact that by definition the geometric mean is less susceptible to outliers than the arithmetic mean.

  16. 16.

    http://www.cyberemotions.eu.

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Acknowledgements

This work was supported by a European Union grant by the 7th Framework Programme, Theme 3: Science of complex systems for socially intelligent ICT. It is part of the CyberEmotions project (contract 231323).

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Authors and Affiliations

  1. School of Mathematics and Computer Science, University of Wolverhampton, Wulfruna Street, Wolverhampton, WV1 1LY, UK

    Georgios Paltoglou & Mike Thelwall

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  1. Georgios Paltoglou
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Correspondence to Georgios Paltoglou .

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  1. Faculty of Physics, Warsaw University of Technology, Warsaw, Poland

    Janusz A. Holyst

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Paltoglou, G., Thelwall, M. (2017). Sensing Social Media: A Range of Approaches for Sentiment Analysis. In: Holyst, J. (eds) Cyberemotions. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-43639-5_6

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