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      A Machine Learning Approach to Correlate Emotional Intelligence and Happiness Based on Twitter Data

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      Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI)

      Human Computer Interaction Conference

      4 - 6 July 2018

      Emotional Intelligence, Happiness, Twitter Scraping, Classifiers, Sentiment Analysis

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          Abstract

          In this study, we have examined the relation between emotional intelligence and happiness. We have identified the traits of high emotionally intelligent people and low emotionally intelligent people and the corresponding words used on twitter to portray those traits. We have scraped twitter and extracted 1000 tweets for each word at three instances of time namely March 2018, 2013 and 2010. Two classifiers namely Support Vector Machine and Naïve Bayes were trained with large data sets to perform sentiment analysis. Each of them classifies a sentence either as positive(happy) or negative(sad). The different sets of scraped tweets corresponding to each word have been used as test data to the above models. Thus, a correlation between emotional intelligence and happiness over time was established. The underlying assumption of the above study is that the individual is expressing his/her true emotion on twitter.

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          Most cited references 8

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          The happy personality: a meta-analysis of 137 personality traits and subjective well-being.

          This meta-analysis used 9 literature search strategies to examine 137 distinct personality constructs as correlates of subjective well-being (SWB). Personality was found to be equally predictive of life satisfaction, happiness, and positive affect, but significantly less predictive of negative affect. The traits most closely associated with SWB were repressive-defensiveness, trust, emotional stability, locus of control-chance, desire for control, hardiness, positive affectivity, private collective self-esteem, and tension. When personality traits were grouped according to the Big Five factors, Neuroticism was the strongest predictor of life satisfaction, happiness, and negative affect. Positive affect was predicted equally well by Extraversion and Agreeableness. The relative importance of personality for predicting SWB, how personality might influence SWB, and limitations of the present review are discussed.
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            Sentiment analysis in Facebook and its application to e-learning

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              Solving the emotion paradox : categorization and the experience of emotion

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                Author and article information

                Contributors
                Conference
                July 2018
                July 2018
                : 1-5
                Affiliations
                Third-year Undergraduate student

                Department of E&ECE, IIT Kharagpur
                Assistant Professor, Centre For Educational Technology, IIT Kharagpur
                Article
                10.14236/ewic/HCI2018.124
                © Shravani et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK.

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Proceedings of the 32nd International BCS Human Computer Interaction Conference
                HCI
                32
                Belfast, UK
                4 - 6 July 2018
                Electronic Workshops in Computing (eWiC)
                Human Computer Interaction Conference
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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