{"type":"ScholarlyArticle","title":"Text Emotion Distribution Learning via Multi-Task Convolutional Neural Network","authors":[{"name":"Yuxiang Zhang","slug":"yuxiang-zhang","given":"Yuxiang","family":"Zhang","affiliation":"College of Computer Science and Technology , Civil Aviation University of China , Tianjin , China"},{"name":"Jiamei Fu","slug":"jiamei-fu","given":"Jiamei","family":"Fu","affiliation":"College of Computer Science and Technology , Civil Aviation University of China , Tianjin , China; College of Computer and Control Engineering , Nankai University , Tianjin , China; Nankai University."},{"name":"Dongyu She","slug":"dongyu-she","given":"Dongyu","family":"She","affiliation":"College of Computer and Control Engineering , Nankai University , Tianjin , China"},{"name":"Ying Zhang","slug":"ying-zhang","given":"Ying","family":"Zhang","affiliation":"College of Computer and Control Engineering , Nankai University , Tianjin , China"},{"name":"Senzhang Wang","slug":"senzhang-wang","given":"Senzhang","family":"Wang","affiliation":"College of Comp. Sci.&Tech. , Nanjing University of Aeronautics and Astronautics , Nanjing , China"},{"name":"Jufeng Yang","slug":"jufeng-yang","given":"Jufeng","family":"Yang","affiliation":"College of Computer and Control Engineering , Nankai University , Tianjin , China"}],"abstract":"Emotion analysis of on-line user generated textual content is important for natural language processing and social media analytics tasks. Most of previous emotion analysis approaches focus on identifying users’ emotional states from text by classifying emotions into one of the finite categories, e.g., joy, surprise, anger and fear. However, there exists ambiguity characteristic for the emotion analysis, since a single sentence can evoke multiple emotions with different intensities. To address this problem, we introduce emotion distribution learning and propose a multi-task convolutional neural network for text emotion analysis. The end-to-end framework optimizes the distribution prediction and classification tasks simultaneously, which is able to learn robust representations for the distribution dataset with annotations of different voters. While most work adopt the majority voting scheme for the ground truth labeling, we also propose a lexiconbased strategy to generate distributions from a single label, which provides prior information for the emotion classification. Experiments conducted on five public text datasets (i.e., SemEval, Fairy Tales, ISEAR, TEC, CBET) demonstrate that our proposed method performs favorably against the state-of-the-art approaches.","doi":"10.24963/ijcai.2018/639","url":"https://ijcai.org/proceedings/2018/639","pdf":"https://ijcai.org/proceedings/2018/0639.pdf","markdown":"https://ijcai.org/proceedings/2018/639.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/639","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Natural Language Processing","track":"Main track","number":639,"pages":{"first":4595,"last":4601},"keywords":["Machine Learning Applications: Applications of Supervised Learning","Natural Language Processing: Sentiment Analysis and Text Mining"],"videos":[],"references":[{"position":1,"raw":"[Agrawal and An, 2012] Ameeta Agrawal and Aijun An. 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