Abstract
AbstractEmotional word spotting has been an important step for the problems of textual emotion recognition and automatic emotion lexicon construction. To express and recognize emotion of words, especially for the words bear undirect emotions, emotion ambiguity, or multiple emotions, the notion of ‘word emotion state’ is proposed, which describes the state of combined basic emotions in a word. On the basis of Ren‐CECps (an annotated emotion corpus) and MaxEnt (maximum entropy) modeling, we explore the effectiveness of several features and their combinations for word emotion recognition in certain contexts. A comparative study on the performances of word emotion and word emotion state recognition is given. The experimental results showed that a model using word emotion state can greatly outperform using word emotion. © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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