Abstract: Online Health Communities (OHCs) provide a platform for patients, caregivers, and researchers to exchange information and support each other. Identifying information that describes patient health experiences in OHCs has many important applications, such as trustworthy knowledge discovery and recommendation. To identify patient experience description, we observe that the same word may have different strengths as an indicator of patient experiences when written by different users. Based on this observation, we propose a User-Word Context Vector model, that holistically captures linguistic features of text, user information and context information to classify patient experiences in OHCs. Experimental evaluation shows that the proposed method significantly outperforms the existing methods on patient experience classification.