HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments Part of the work was done while Asai, Evensen, and Xu were at Recruit Institute of Technology.
Table 10: Precision/recall/F1 scores for each category as well as the % of moments per category for each reflection period. All differences between the reflection periods are statistically significant at p<10−5 except for bonding.
['[BOLD] Category', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1', '[BOLD] % of moments  [BOLD] 24 Hrs', '[BOLD] % of moments  [BOLD] 3 Months']
We developed a multi-class classifier using Logistic Regression with a bag of words representation of happy moments as features. To obtain training data, we crowdsourced a batch of 15,000 happy moments to obtain category labels. Every happy moment was shown to 5 workers, and we only considered labels that at least 3 workers agreed on. Clearly the classifier has room for further improvement on categories such as “Leisure” and “Enjoy the moment” which shows that word distributions are not sufficient for this task. Building a classifier with sufficiently high precision/recall scores on all categories can be a challenging task in general, as it usually involves inferring some information or context that is not explicitly mentioned in text. Notice that HappyDB has roughly the same number of moments for each reflection period. The higher frequency of moments in “Exercise”, “Nature”, and “Leisure” under the 24 hours reflection period confirms our theory that daily tasks are sources of short-term happiness. Longer-term happiness is more likely to come from loved ones or achievements.