Depression has long been studied in the NLP field, with most works focusing on individuals’ negative emotions. People with depression also experience happiness, but this was not extensively studied.Previous works have shown that approaches relying on sentiment or emotion classification are unsuitable for extracting the expressions of feelings that bring happiness to an individual because they may not be expressed in positive words only. In this work, we conduct a large-scale study of happy moments from social media texts of depressed and non-depressed individuals. We develop an extensive deep learning-based framework to extract happy moments from text, and annotate them with semantic topics, gender labels, and agency and sociality measures. We analyze over 400,000 happy moments and show significant differences in topics, agency, and sociality of depressed and not-depressed users, varying by gender. Both male and female users with depression expressed less sociality in their happy moments than control users. Male users’ agency was not impaired in depression, while female users with depression expressed fewer happy moments with agency than the control group. Our research can inform psychology interventions, which can foster feelings of longer-lasting happiness and represent a promising path of collaboration between computational linguistics and psychology.