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Many users join online health communities (OHC) to obtain information and seek social support. Understanding the emotional impacts of participation on patients and their informal caregivers is important for OHC managers. Ethnographical observations, interviews, and questionnaires have reported benefits from online health communities, but these approaches are too costly to adopt for large-scale analyses of emotional impacts. A computational approach using machine learning and text mining techniques is demonstrated using data from the American Cancer Society Cancer Survivors Network (CSN), an online forum of nearly a half million posts. This approach automatically estimates the sentiment of forum posts, discovers sentiment change patterns in CSN members, and allows investigation of factors that affect the sentiment change. This first study of sentiment benefits and dynamics in a large-scale health-related electronic community finds that an estimated 75%-85% of CSN forum participants change their sentiment in a positive direction through online interactions with other community members. Two new features, Name and Slang, not previously used in sentiment analysis, facilitate identifying positive sentiment in posts. This work establishes foundational concepts for further studies of sentiment impact of OHC participation and provides insight useful for the design of new OHC's or enhancement of existing OHCs in providing better emotional support to their members.