{"id":569178,"date":"2019-02-21T16:46:07","date_gmt":"2019-02-22T00:46:07","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=569178"},"modified":"2019-02-21T17:07:02","modified_gmt":"2019-02-22T01:07:02","slug":"identifying-empathetic-messages-in-online-health-communities","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/identifying-empathetic-messages-in-online-health-communities\/","title":{"rendered":"Identifying Empathetic Messages in Online Health Communities"},"content":{"rendered":"
Empathy captures one\u2019s ability to correlate with and understand others\u2019 emotional states and experiences. Messages with empathetic content are considered as one of the main advantages for joining online health communities due to their potential to improve people\u2019s moods. Unfortunately, to this date, no computational studies exist that automatically identify empathetic messages in online health communities. We propose a combination of Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks, and show that the proposed model outperforms each individual model (CNN and LSTM) as well as several baselines.<\/p>\n","protected":false},"excerpt":{"rendered":"
Empathy captures one\u2019s ability to correlate with and understand others\u2019 emotional states and experiences. Messages with empathetic content are considered as one of the main advantages for joining online health communities due to their potential to improve people\u2019s moods. Unfortunately, to this date, no computational studies exist that automatically identify empathetic messages in online health 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