Detecting fine-grained emotions in online
\nhealth communities provides insightful information about patients\u2019 emotional states.
\nHowever, current computational approaches to
\nemotion detection from health-related posts
\nfocus only on identifying messages that contain emotions, with no emphasis on the emotion type, using a set of handcrafted features.
\nIn this paper, we take a step further and propose to detect fine-grained emotion types from
\nhealth-related posts and show how high-level
\nand abstract features derived from deep neural networks combined with lexicon-based features can be employed to detect emotions.<\/p>\n","protected":false},"excerpt":{"rendered":"
Detecting fine-grained emotions in online health communities provides insightful information about patients\u2019 emotional states. However, current computational approaches to emotion detection from health-related posts focus only on identifying messages that contain emotions, with no emphasis on the emotion type, using a set of handcrafted features. In this paper, we take a step further and propose 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