Mental health is an ever-growing issue of concern, especially in light of the COVID pandemic. In this context, we study big data from social media over a 7-year time span to gauge evolving perceptions of mental health, and discuss our research findings, potentially useful for decision support in healthcare. We deploy topic modeling and sentiment analysis to estimate public perceptions of mental health issues, focusing on Twitter as the social media site. We claim that it is important to consider polarity as well as subjectivity in sentiment analysis to comprehend two different aspects of sentiment, i.e. orientation in the emotion, and extent of fact vs. opinion. We assert that ranking via topic modeling is beneficial to fathom the relative importance of issues over the years. We harness tools/techniques from natural language processing and data mining to discover knowledge from big data on social media, related to mental health. Some of our findings reveal that the sentiment around mental health has remained positive overall, but has decreased since the beginning of the COVID pandemic. Major events, such as elections and the pandemic, greatly impact the conversation surrounding mental health. Some topics have remained consistent throughout the years. In other topics, the tone of the public discussions has shifted. The outcomes of our study would be useful to a variety of professionals, ranging from data scientists to epidemiologists and psychologists. This work impacts big healthcare data in general.