A Classifier Based Approach to Emotion Lexicon Construction
The present task of developing an emotion lexicon shows the differences from the existing solutions by considering the definite as well as fuzzy connotation of the emotional words into account. A weighted lexical network has been developed on the freely available ISEAR dataset using the co-occurrence threshold. Two methods were applied on the network, a supervised method that predicts the definite emotion orientations of the words which received close or equal membership values from the first method, Fuzzy c-means clustering. The kernel functions of the two methods were modified based on the similarity based edge weights, Point wise Mutual Information (PMI) and universal Law of Gravitation (LGr) between the word pairs. The system achieves the accuracy of 85.92% in identifying emotion orientations of the words from the WordNet Affect based lexical network.
KeywordsEmotion orientations ISEAR Fuzzy Clustering SVM PMI Law of Gravitation WordNet Affect
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