Document Type

Conference Proceeding

Publication Date

2019

Publisher

IEEE

Source Publication

Proceedings of the 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)

Source ISSN

978-1-7281-2607-4

Abstract

In this research paper, we analyzed what moments and activities make people happy, based on a collection of happy moments. We are focusing on specific happy moments from a collection of text responses that people have shared through the crowd-sourcing platform: Amazon Mechanical Turk (MTurk). Using crowd-sourcing to collect our data allows us to advance our understanding of the cause of happiness, by focusing on words and real human experiences. Workers of MTurk were asked to reflect on what makes them happy in a given period and share three specific moments in complete sentences. Through text-based analysis, we will look to see what other components have a role in making a specific event happy and further analyze how we can classify such words. Also, we dive deeper into specific subcategories of classifiers in an attempt to form insights about their happiness level based on specific factors. With the goal to extract features from the text in HappyDB, in this study we used the bag of words approach. Through doing so, our results were successful at predicting the happiness category, concerning both accuracy and context. Our models were able to accomplish the goal of understanding a happy moment and fit such a moment into one of the seven ground truth happiness categories we set at the beginning of this study. We finished the article with the ethical perspective of such research works and related social implications.

Comments

Accepted version. Published as part of the proceedings of the IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC) 2019: 653-662. DOI. © 2019 Institute of Electrical and Electronic Engineers (IEEE). Used with permission.

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