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• W170592701 abstract "Emotion words have been well used as the most obvious choice as feature in the task of textual emotion recognition and automatic emotion lexicon construction. In this work, we explore features for recognizing emotion. Based on Ren-CECps (an annotated emotion corpus) and MaxEnt (Maximum entropy) model, several contextual features and their combination have been experimented. Then PLSA (probabilistic latent semantic analysis) is used to get semantic feature by clustering words and sentences. The experimental results demonstrate the effectiveness of using semantic feature for emotion recognition. After that, word emotion is proposed to describe the combined basic emotions in a word. A significant performance improvement over contextual and semantic features was observed after adding emotion components as feature." @default.
• W170592701 created "2016-06-24" @default.
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• W170592701 date "2010-08-23" @default.
• W170592701 modified "2023-09-26" @default.
• W170592701 title "An Exploration of Features for Recognizing Word Emotion" @default.
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