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A Borda count for collective sentiment analysis

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Abstract

Sentiment analysis assigns a positive, negative or neutral polarity to an item or entity, extracting and aggregating individual opinions from their textual expressions by means of natural language processing tools. In this paper we observe that current sentiment analysis techniques are satisfactory in case there is a single entity under consideration, but can lead to inaccurate or wrong results when dealing with a set of multiple items. We argue in favor of importing techniques from voting theory and preference aggregation to provide a more accurate definition of the collective sentiment over a set of multiple items. We propose a notion of Borda count which combines individuals’ sentiment with comparative preference information, we show that this class of rules satisfies a number of properties which have a natural interpretation in the sentiment analysis domain, and we evaluate its behavior when faced with highly incomplete domains.

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Author information

Author notes

  1. Umberto Grandi

    Present address: IRIT, University of Toulouse, Toulouse, France

  2. Francesca Rossi

    Present address: IBM T. J. Watson Research center, Yorktown Heights, NY, USA

Authors and Affiliations

  1. Department of Mathematics, University of Padova, Via Trieste 63, 35121, Padova, Italy

    Umberto Grandi, Andrea Loreggia & Francesca Rossi

  2. IBM TJ Watson Research Laboratory, P.O. Box 704, Yorktown Heights, New York, NY, 10598, USA

    Vijay Saraswat

Authors
  1. Umberto Grandi
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  2. Andrea Loreggia
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  3. Francesca Rossi
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  4. Vijay Saraswat
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Correspondence to Umberto Grandi.

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Grandi, U., Loreggia, A., Rossi, F. et al. A Borda count for collective sentiment analysis. Ann Math Artif Intell 77, 281–302 (2016). https://doi.org/10.1007/s10472-015-9488-0

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  • DOI: https://doi.org/10.1007/s10472-015-9488-0

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