A Consensus Approach to the Sentiment Analysis Problem Driven by Support-Based IOWA Majority

Orestes Appel, Francisco Chiclana, Jenny Carter, Hamido Fujita

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


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 utilizes 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 represent what the majority think. During the experimental phase, the use of the IOWA operator coupled with the linguistic quantifier ‘most’ (math formula) proved to yield superior results compared to those achieved when utilizing other techniques commonly applied when some sort of averaging is needed, such as arithmetic mean or median techniques.
Original languageEnglish
Pages (from-to)947-965
Number of pages19
JournalInternational Journal of Intelligent Systems
Issue number9
Early online date9 Feb 2017
Publication statusPublished - Sep 2017
Externally publishedYes


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