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The current study investigated the sentiment dynamics in patients\u2019 narratives in a Breast Cancer community group ( Breastcancer.org ) to identify the changes in emotions, thoughts, stress, and coping mechanisms while undergoing treatment options, particularly chemotherapy, radiation, and surgery. Sentiment dynamics of users\u2019 posts was performed using a deep learning model. A sentiment change analysis was performed to measure change in the satisfaction level of the users. The deep learning model BiLSTM with sentiment embedding features provided a better F1-score of 91.9%. Sentiment dynamics can assess the difference in satisfaction level the users acquire by interacting with other users in the forum. A comparison of the proposed model with existing models revealed the effectiveness of this methodology. <\/jats:p>","DOI":"10.1177\/14604582211007537","type":"journal-article","created":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T06:56:02Z","timestamp":1617951362000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":16,"title":["Deep learning based analysis of sentiment dynamics in online cancer community forums: An experience"],"prefix":"10.1177","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7830-2461","authenticated-orcid":false,"given":"Athira","family":"Balakrishnan","sequence":"first","affiliation":[]},{"given":"Sumam Mary","family":"Idicula","sequence":"additional","affiliation":[{"name":"Cochin University of Science and Technology, India"}]},{"given":"Josette","family":"Jones","sequence":"additional","affiliation":[{"name":"Indiana University, 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