Emotion is an instinctive state of mind aroused by some specific objects or situation. Exchange of textual information is an important medium for communication and contains a rich set of emotional expressions. The computational approaches to emotion analysis in textual data require annotated lexicons with polarity tags. In this paper we propose a novel method for constructing emotion lexicon annotated with Ekman"s six basic emotion classes (anger, disgust, fear, happy, sad and surprise). We adopt the Potts model for the probability modeling of the lexical network. The lexical network has been constructed by connecting each pair of words in which one of the two words appears in the gloss of the other. Starting with a small number of emotional seed words, the emotional categories of other words have been determined. With manual checking of top 200 words from each class an average precision of 85.41% has been achieved.
REF adopted the Potts model for the probability modeling of the lexical network that was constructed by connecting each pair of words in which one of the two words appears in the gloss of the other.
Construction of Emotional Lexicon Using Potts Model
IJCNLP
Computer Science