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Ordered weighted averaging for emotion-driven polarity detection - 2021

Ordered Weighted Averaging For Emotion-Driven Polarity Detection

Research Area:  Machine Learning


The overall rating of an opinion can generally be considered as the aggregation of the individual ratings of all features of that opinion. Nevertheless, there are cases in which the overall rating differs substantially from the mean or weighted mean of the ratings of the individual features. These cases can be explained in terms of user mood. To address this problem, this study introduces a fuzzy framework for computing user mood based on SenticNet and sentic patterns, which are used to guide an ordered weighted averaging operator. This operator allows the aggregation to be computed in such a way as to provide an understanding of why some positive or negative aspects are considered to a greater or lesser extent. The performance and advantages of this proposal are illustrated in depth via a variety of scenarios applied to real data. The results show a promising framework applicable to other tools, such as customized recommender systems or decision support systems.


Author(s) Name:  Jesus Serrano-Guerrero, Francisco P. Romero & Jose A. Olivas

Journal name:  Cognitive Computation

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s12559-021-09837-6

Volume Information:   volume 14, pages 194–211 (2022)