What Constitutes Happiness? Predicting and Characterizing the Ingredients of Happiness Using Emotion Intensity Analysis
What Constitutes Happiness? Predicting and Characterizing the Ingredients of Happiness Using Emotion Intensity Analysis
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幸福是什么？
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Yinping Yang
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作者：
Raj Kumar Gupta;Prasanta Bhattacharya;Yinping Yang
本文探讨了使用情绪强度分析预测和理解幸福的成分所表达的文本。我们表明，通过使用情感强度特征的五个维度（即，喜悦、愤怒、恐惧、悲伤和总体效价），我们可以在分类代理方面实现良好的准确性（即，快乐时刻的作者是否在控制中）（ACC= 73.8%，AUC= 0.579，F1= 0.849）和在分类社会（即，无论快乐时刻是否涉及其他人）（ACC= 60.3%，AUC= 0.637，F1= 0.603）。通过将情感强度与情感，语言学，人口统计学，概念和单词嵌入特征相结合，我们最终的混合模型在代理（ACC= 83.5%，AUC= 0.887，F1= 0.893）和社交（ACC= 90.3%，AUC= 0.959，F1= 0.907）预测方面表现得更好。此外，我们发现了情绪强度如何在各种概念中表征幸福表达的有趣模式（例如，家庭，食物，职业，动物），两个反思期之间（24小时与3个月），以及七个用户生成的内容语料库（HappyDB与MySpace，Runners World，Twitter，Digg，BBC和YouTube）。
This paper explores the use of emotion intensity analysis in predicting and understanding the ingredients of happiness as expressed in text. We show that by using just the five dimensions of emotion intensity features (i.e., joy, anger, fear, sadness and overall valence), we can achieve good accuracies in classifying agency (i.e., whether or not the author of the happy moment is in control) (ACC=73.8%, AUC=.579, F1=.849) and in classifying social (i.e., whether or not the happy moment involves other people) (ACC=60.3%, AUC=.637, F1=.603). By integrating emotion intensity with sentiment, linguistics, demographics, concepts, and word embedding features, our final hybrid model performed significantly better for agency (ACC=83.5%, AUC=.887, F1=.893) and for social (ACC=90.3%, AUC=.959, F1=.907) predictions. Furthermore, we uncovered interesting patterns in how emotion intensities characterized happiness expressions across the various concepts (e.g., family, food, career, animals), between the two reflection periods (24 hours vs. 3 months), and across seven user-generated content corpora sources (HappyDB vs. MySpace, Runners World, Twitter, Digg, BBC and YouTube).