An approach to improve the accuracy of probabilistic classifiers for decision support systems in sentiment analysis

García‐Díaz, Vicente; Espada, Jordán Pascual; Crespo, Rubén González; G-Bustelo, B. Cristina Pelayo; Lovelle, Juan Manuel Cueva

doi:10.1016/j.asoc.2017.05.038

Cited by 32 publications

(17 citation statements)

References 42 publications

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“…In Waseem et al [57], ML techniques were used with multiple intermediate processing tasks to improve assertiveness. Similar to the results obtained by Garcia-Diaz et al [51], this process presented improvement of results but loss of time performance. Agarwal and Sureka [13] proposed a lexical dictionary to assist the detection of racism in posts on Tumblr.…”

Section: Machine Learning and Mixed Learning Based Solutionssupporting

confidence: 89%

“…Most of the analyzed investigations use machine learning to evaluate sentiment. Natural language processing techniques are explored in the approaches proposed in [52,58,60,[62][63][64]74,75]; data mining and semantic analysis are used in [51]. In general, the studies presented better assertiveness measures in the categorization of sentiment using complementary approaches (e.g., semantic descriptors with ML) as compared with a single one.…”

Section: Discussion On Related Workmentioning

confidence: 99%

“…Naive Bayes classifiers are used for extensive text classification, based on word frequency assessment as characteristics [51]. This is a scalable classifier, which offers good empirical results [51,52].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

“…Garcia-Diaz et al [51] defined the process of emotion extraction as "opinion mining", which consists of using natural language processing and computational linguistics. The data mining process takes place in five steps, namely: selection, preprocessing, transformation, data mining, and interpretation.…”

Section: Machine Learning and Mixed Learning Based Solutionsmentioning

confidence: 99%

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A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

et al. 2020

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Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.

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Section: Machine Learning and Mixed Learning Based Solutionssupporting

confidence: 89%

Section: Discussion On Related Workmentioning

confidence: 99%

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Section: Machine Learning and Mixed Learning Based Solutionsmentioning

confidence: 99%

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A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

et al. 2020

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“…In order to indicate the linguistic information specifically, the extended linguistic term sets have been defined, such as the hesitant fuzzy linguistic term sets [5], the linguistic intuistionstic fuzzy sets [6], and the probabilistic linguistic term sets [7], to enrich the study of MADM. The study of multi-criteria decision aid (MCDA) has attracted many experts: Morente-Molinera [8] proposed the fuzzy ontologies and multi-granular linguistic modeling methods to solve the MADM problem; W. Sałabun [9] settled the MADM problem without the rank reversal phenomenon; S. Faizi [10] presented the characteristic objects method to handle the group decision-making problem; Garca-Daz [11] introduced the probabilistic classifiers into the study of MADM problem.…”

Section: Introductionmentioning

confidence: 99%

A New Fuzzy Multiple Attribute Decision Making Method Based on the Utility Transformation Functions

Zuo

Chen

2019

Symmetry

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The 2-tuple linguistic information model (2TLIM) is a useful tool to avoid the loss of information, which has been widely adapted in the study of the multiple attribute decision making (MADM) problem. However, there is a limitation, the limitation is that the difference between the neighboring 2-tuple linguistic information is fixed regardless of the decision-makers’ attitude. In this paper, we define the utility transformation functions based on the 2-tuple linguistic utility to overcome the drawback. Firstly, by introducing the economic utility theory, the 2-tuple linguistic utility is defined, the 2-tuple linguistic utility parameter (2TLUP) and the 2-tuple linguistic marginal utility (2TLMU) are constructed to achieve the measurement of the decision-makers’ attitude. The utility transformation functions are developed on the decision-makers’ attitude. Secondly, the 2-tuple linguistic operational laws are presented with the extended Hamacher T-norm (TN) and T-conorm (TC). Subsequently, we propose the 2-tuple linguistic utility weighted average (2TLUWA) operator and the method of MADM. Lastly, the application and the comparison with the existing methods are summarized to verify the practicality and advantages of the proposed method of MADM.

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Machine Learning Based Indoor Localization Using Wi-Fi and Smartphone in a Shopping Malls

Maaloul

Abdelhamid

Lejdel

2022

Artificial Intelligence and Its Applications

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An approach to improve the accuracy of probabilistic classifiers for decision support systems in sentiment analysis

Cited by 32 publications

References 42 publications

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

A New Fuzzy Multiple Attribute Decision Making Method Based on the Utility Transformation Functions

Machine Learning Based Indoor Localization Using Wi-Fi and Smartphone in a Shopping Malls

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