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1
Some Trends in Fuzzy Decision Making
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2
Consistency-driven methodology to manage incomplete linguistic preference relation: A perspective based on personalized individual semantics
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3
Consensus in Sentiment Analysis
Carter, Jenny; Chiclana, Francisco; Fujita, Hamido; Appel, Orestes. - : Springer International Publishing, 2021
Abstract: The objective of this chapter is to present a method applicable in group decision-making where computing the opinion of the majority of participants is key. In this article, we present a method that makes use of Induced Ordered Weighted Averaging (IOWA) operators to aggregate a majority opinion out of a number of Sentiment Analysis (SA) classification systems. The numerical output of each SA classification method is used as input to a carefully chosen IOWA operator that is semantically equivalent 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 thinks.
Keyword: Consensus Majority support; Ordered weighted averaging; OWA Induced ordered weighted averaging; Sentiment aggregation; Sentiment analysis; Sentiment analysis Consensus Majority support Sentiment aggregation Ordered weighted averaging OWA Induced ordered weighted averaging
URL: https://dora.dmu.ac.uk/handle/2086/20823
https://doi.org/10.1007/978-3-030-66474-9_3
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4
Sentiment Analysis using TF-IDF Weighting of UK MPs’ Tweets on Brexit
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5
Proportional hesitant 2-tuple linguistic distance measurements and extended VIKOR method: Case study of evaluation and selection of green airport plans
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6
A Fuzzy Approach to Sentiment Analysis at the Sentence Level
Appel, Orestes; Chiclana, Francisco; Carter, Jenny. - : Springer International Publishing, 2021
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7
Fuzzy convolutional deep-learning model to estimate the operational risk capital using multi-source risk events
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8
Type-1 OWA Operators in Aggregating Multiple Sources of Uncertain Information: Properties and Real-World Applications in Integrated Diagnosis.
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9
Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality
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10
Consistency improvement with a feedback recommendation in personalized linguistic group decision making
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11
ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messages [<Journal>]
Guellil, Imane [Verfasser]; Azouaou, Faical [Verfasser]; Chiclana, Francisco [Verfasser]
DNB Subject Category Language
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12
Attitude Quantifier Based Possibility Distribution Generation Method for Hesitant Fuzzy Linguistic Group Decision Making
Hao, Jingjing; Chiclana, Francisco. - : Elsevier, 2020
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13
Multi-stage consistency optimization algorithm to decision-making with incomplete probabilistic linguistic preference relation
Wang, Peng; Liu, Peide; Chiclana, Francisco. - : Elsevier, 2020
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14
Revisiting Fuzzy and Linguistic Decision-Making: Scenarios and Challenges for Wiser Decisions in a Better Way
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15
ArAutoSenti: Automatic annotation and new tendencies for sentiment classification of Arabic messages
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16
Personalized individual semantics-based approach for large scale failure mode and effect analysis with incomplete preference information
Dong, Yucheng; Zhang, Hengjie; Chiclana, Francisco. - : Taylor & Francis Group, 2020
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17
Consensus and opinion evolution-based failure mode and effect analysis approach for reliability management in social network and uncertainty contexts
Zhang, Hengjie; Dong, Yucheng; Xiao, Jing. - : Elsevier, 2020
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18
Type-1 OWA operators in aggregating multiple sources of uncertain information : properties and real world applications
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19
Dealing with incomplete information in linguistic group decision making by means of Interval Type‐2 Fuzzy Sets
In: ISSN: 0884-8173 ; EISSN: 1098-111X ; International Journal of Intelligent Systems ; https://www.hal.inserm.fr/inserm-03026626 ; International Journal of Intelligent Systems, Wiley, 2019, 34 (6), pp.1261-1280. &#x27E8;10.1002/int.22095&#x27E9; (2019)
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20
Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects
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