Ordered Weighted Averaging for Emotion-Driven Polarity Detection

Abstract

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.

A fuzzy aspect‐based approach for recommending hospitals

“…In the second study case, the use of the IFOWA operator proves that it is possible to model different scenarios according to the user characteristics. The automatic calculation of those characteristics as in Reference [49] is an interesting future research line to study new scenarios.…”

“…To compute this value, many algorithms and tools are available which could be used. [48][49][50][51][52][53] Analyzing each aspect i from each opinion k from the hospital j, a positive sentiment orientation A ik j can be interpreted as a vote in favor, a neutral sentiment can indicate hesitation as well as neutrality or objectivity, lack of sentiment, and finally, a negative sentiment orientation can be considered as a vote against that aspect. Mathematically, the percentage of positive/neutral/negative reviews towards aspect i of hospital H j can be defined as where…”

Some Single-Valued Neutrosophic Uncertain Linguistic Maclaurin Symmetric Mean Operators and Their Application to Multiple-Attribute Decision Making

“…As an effective tool, aggregation operators are widely used in MADM problems [16]. Yager [17] proposed the ordered weighted average (OWA) operator [18] for multi-attribute decision making. Bonferroni presented the Bonferroni mean (BM) [19][20][21][22] operator, which can capture the interrelationships among multiple input arguments.…”

Comparing global news sentiment using hesitant linguistic terms

“…These types of methods have been applied, in general, to obtain information from text reviews. For example in Reference [22], an Ordered Weighted Averaging (OWA) operator is applied to determine the polarity of product features assessed in a review. Second, different multicriteria decision models are used for ranking or comparing information.…”