Computing and Library Services - delivering an inspiring information environment

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

Appel, Orestes, Chiclana, Francisco, Carter, Jenny and Fujita, Hamido (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. ISSN 0884-8173

PDF - Accepted Version
Download (554kB) | Preview


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.

Item Type: Article
Subjects: T Technology > T Technology (General)
Related URLs:
Depositing User: Harriet Notman
Date Deposited: 06 Feb 2018 12:36
Last Modified: 28 Aug 2021 15:16


Downloads per month over past year

Repository Staff Only: item control page

View Item View Item

University of Huddersfield, Queensgate, Huddersfield, HD1 3DH Copyright and Disclaimer All rights reserved ©