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    A consensus approach to sentiment analysis

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    Author's copy of accepted paper. (300.3Kb)
    Date
    2017-06-04
    Author
    Appel, Orestes;
    Chiclana, Francisco;
    Carter, Jenny;
    Fujita, Hamido
    Metadata
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    Abstract
    There are many situations where the opinion of the majority of participants is critical. The scenarios could be multiple, like a number of doctors finding commonality on the diagnosing of an illness or parliament members looking for consensus on a specific law being passed. In this article we present a method that utilises Induced Ordered Weighted Averaging (IOWA) operators to aggregate a majority opinion from a number of Sentiment Analysis (SA) classification systems, where the latter occupy the role usually taken by human decision-makers. Previously determined sentence intensity polarity by different SA classification methods are used as input to a specific IOWA operator. During the experimental phase, the use of the IOWA operator coupled with the linguistic quantifier `most' (IOWA_most) proved to yield superior results compared to those achieved when utilising other techniques commonly applied when some sort of averaging is needed, such as arithmetic mean or median techniques.
    Description
    Citation : Appel O., Chiclana F., Carter J., Fujita H. (2017) A Consensus Approach to Sentiment Analysis. In: Benferhat S., Tabia K., Ali M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science, vol 10350. Springer, Cham
    URI
    http://hdl.handle.net/2086/13366
    DOI
    https://doi.org/10.1007/978-3-319-60042-0_69
    Research Group : Centre for Computational Intelligence
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
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    • School of Computer Science and Informatics [2679]

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