A consensus approach to the sentiment analysis problem driven by support-based IOWA majority
In group decision-making 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 diagnose of an illness or parliament members looking for consensus on an 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 as typically seen in group decision situations. In this case, the numerical outputs of different SA classification methods are used as input to a specific IOWA operator that is semantically close to the fuzzy linguistic quantifier 'most of'. The object of the aggregation will be the intensity of the previously determined sentence polarity in such a way that the results represents what the majority think. 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.
Citation : Appel, O., Chiclana, F., Carter, J. and Fujita, H. (2017) A consensus approach to the sentiment analysis problem driven by support-based IOWA majority. International Journal of Intelligent Systems, 32 (9), pp. 947-965
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes