Fuzzy Rule-based Classifier Design with Co-Operative Bionic Algorithm for Opinion Mining Problems

Akhmedova, Shakhnaz; Semenkin, ‪Eugene; Stanovov, Vladimir

doi:10.5220/0005974700680074

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Thus, the idea was to generate ANN with a relatively simple structure which would effectively solve a given classification problem. Also a population-based optimization method called Co-Operation of Biology Related Algorithms or shortly COBRA [8] was applied for the automated generation of fuzzy rule-based classifiers [9].…”

Section: Introductionmentioning

confidence: 99%

Automatically Generated Data Mining Tools for Complex System Operator's Condition Detection Using Non-Contact Vital Sensing

Akhmedova

Stanovov

Vishnevskaya

et al. 2021

IEICE Trans. Commun.

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This study is focused on the automated detection of a complex system operator's condition. For example, in this study a person's reaction while listening to music (or not listening at all) was determined. For this purpose various well-known data mining tools as well as ones developed by authors were used. To be more specific, the following techniques were developed and applied for the mentioned problems: artificial neural networks and fuzzy rule-based classifiers. The neural networks were generated by two modifications of the Differential Evolution algorithm based on the NSGA and MOEA/D schemes, proposed for solving multiobjective optimization problems. Fuzzy logic systems were generated by the population-based algorithm called Co-Operation of Biology Related Algorithms or COBRA. However, firstly each person's state was monitored. Thus, databases for problems described in this study were obtained by using non-contact Doppler sensors. Experimental results demonstrated that automatically generated neural networks and fuzzy rule-based classifiers can properly determine the human condition and reaction. Besides, proposed approaches outperformed alternative data mining tools. However, it was established that fuzzy rule-based classifiers are more accurate and interpretable than neural networks. Thus, they can be used for solving more complex problems related to the automated detection of an operator's condition.

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Section: Introductionmentioning

confidence: 99%

Automatically Generated Data Mining Tools for Complex System Operator's Condition Detection Using Non-Contact Vital Sensing

Akhmedova

Stanovov

Vishnevskaya

et al. 2021

IEICE Trans. Commun.

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Sentiment analysis using fuzzy logic: A comprehensive literature review

Vashishtha

Gupta

Mittal

2023

WIREs Data Min & Knowl

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Understanding and comprehending humans' views, beliefs, attitudes, or opinions toward a particular entity is sentiment analysis (SA). Advancements in e‐commerce platforms has led to an abundance of the real‐time and free forms of opinions floating on social media platforms. This real‐world data are imprecise and vague hence fuzzy logic is required to deal with such subjective data. Since opinions can be fuzzy in nature and definitions of opinion words can be elucidated differently; fuzzy logic has witnessed itself as an effective method to capture the expression of opinions. The study presents an elaborate review of the around 170 published research works for SA using fuzzy logic. The primary emphasis is focused on text‐based SA, audio‐based SA, and fusion of text‐audio features‐based SA. This article discusses the various novel ways of classifying fuzzy logic‐based SA research articles, which have not been accomplished by any other review article till date. The article puts forward the importance of SA tasks and identifies how fuzzy logic adds to this importance. Finally, the article outlines a taxonomy for sentiment classification based on the technique‐supervised and unsupervised in the SA models and comprehensively reviews the SA approaches specific to their task. Prominently, this study highlights the suitability of fuzzy‐based SA approaches into five different classes vis‐a‐vis (a) Sentiment Cognition from Words using fuzzy logic, (b) Sentiment Cognition from Phrases using fuzzy logic, (c) Fuzzy‐rule based SA, (d) Neuro‐fuzzy network‐based SA, and (e) Fuzzy Emotion Recognition.This article is categorized under: Algorithmic Development > Text Mining Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

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Co-operation of Biology Related Algorithms for Solving Opinion Mining Problems by Using Different Term Weighting Schemes

Akhmedova

Semenkin

Stanovov

2017

Informatics in Control, Automation and Robotics

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Fuzzy Rule-based Classifier Design with Co-Operative Bionic Algorithm for Opinion Mining Problems

Cited by 4 publications

References 11 publications

Automatically Generated Data Mining Tools for Complex System Operator's Condition Detection Using Non-Contact Vital Sensing

Automatically Generated Data Mining Tools for Complex System Operator's Condition Detection Using Non-Contact Vital Sensing

Sentiment analysis using fuzzy logic: A comprehensive literature review

Co-operation of Biology Related Algorithms for Solving Opinion Mining Problems by Using Different Term Weighting Schemes

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