A Data Mining Approach for Analyzing Personality, Cognitive and Emotional Features of Social Network Consumers


Authors : Constantinos Halkiopoulos; Hera Antonopoulou; Evgenia Gkintoni

Volume/Issue : Volume 6 - 2021, Issue 4 - April

Google Scholar : http://bitly.ws/9nMw

Scribd : https://bit.ly/3vFWVs2

Abstract : The current research examines the personality characteristics and emotional intelligence of young adult consumers who shop through social media. Consumer behaviour has long piqued the research community's curiosity. Contemporary customer behavior analysis considers a wide variety of influences affecting the consumer and recognizes a wide variety of buying behaviors other than shopping. Due to the fact that online sales are now an everyday occurrence, it is beneficial to research online customers who are social media users who shop via social media. Personality traits and emotional characteristics, as described in emotional intelligence, are two critical components that affect consumer behaviour. Emotional intelligence is a component of personality and intellectual ability that is inherited by one's parents and grows - develops over one's lifespan. The term "personality" refers to the pattern of emotions, feelings, and actions that distinguishes individuals from one another. These have an effect on how an individual thinks, feels, and behaves against itself and others. The results were gathered by having participants complete the self-report questionnaire Trait Emotional Intelligence (TEIQue) for emotional intelligence and Eysenck Personality Questionnaire (EPQ) for personality characteristics associated with personality disorders. The collected data were then chosen for review, undergoing necessary transformations to ensure that they were in a format appropriate for implementation of the respective machine learning algorithms provided in the R Software. Additionally, the appropriate set of algorithm parameters was calculated based on the implementation scenario in order to generate inference rules. Several algorithms were introduced in response to particular research concerns, including classification algorithms for the generation of decision trees based on the four more general factors of emotional intelligence (welfare, selfcontrol, emotionality, and sociability), as well as personality characteristics of social network users. Following a weighting and criterion-based analysis, the findings obtained present consumers' ratings, which are used to determine the degree of emotional intelligence and personality traits. Personality and emotional intelligence indices may be critical in elucidating social network users' consumer behaviour.

Keywords : Consumer Behaviour, Emotional Intelligence, Personality, Data Mining.

The current research examines the personality characteristics and emotional intelligence of young adult consumers who shop through social media. Consumer behaviour has long piqued the research community's curiosity. Contemporary customer behavior analysis considers a wide variety of influences affecting the consumer and recognizes a wide variety of buying behaviors other than shopping. Due to the fact that online sales are now an everyday occurrence, it is beneficial to research online customers who are social media users who shop via social media. Personality traits and emotional characteristics, as described in emotional intelligence, are two critical components that affect consumer behaviour. Emotional intelligence is a component of personality and intellectual ability that is inherited by one's parents and grows - develops over one's lifespan. The term "personality" refers to the pattern of emotions, feelings, and actions that distinguishes individuals from one another. These have an effect on how an individual thinks, feels, and behaves against itself and others. The results were gathered by having participants complete the self-report questionnaire Trait Emotional Intelligence (TEIQue) for emotional intelligence and Eysenck Personality Questionnaire (EPQ) for personality characteristics associated with personality disorders. The collected data were then chosen for review, undergoing necessary transformations to ensure that they were in a format appropriate for implementation of the respective machine learning algorithms provided in the R Software. Additionally, the appropriate set of algorithm parameters was calculated based on the implementation scenario in order to generate inference rules. Several algorithms were introduced in response to particular research concerns, including classification algorithms for the generation of decision trees based on the four more general factors of emotional intelligence (welfare, selfcontrol, emotionality, and sociability), as well as personality characteristics of social network users. Following a weighting and criterion-based analysis, the findings obtained present consumers' ratings, which are used to determine the degree of emotional intelligence and personality traits. Personality and emotional intelligence indices may be critical in elucidating social network users' consumer behaviour.

Keywords : Consumer Behaviour, Emotional Intelligence, Personality, Data Mining.

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