Identification and Classification of Emotional Key Phrases from Psycho- logical Texts

Emotions, a complex state of feeling results in physical and psychological changes that influence human behavior. Thus, in order to extract the emotional key phrases from psychological texts, here, we have presented a phrase level emotion identification and classification system. The system takes pre-defined emotional statements of seven basic emotion classes (anger, disgust, fear, guilt, joy, sadness and shame) as input and extracts seven types of emotional trigrams. The trigrams were represented as Context Vectors. Between a pair of Context Vectors, an Affinity Score was calculated based on the law of gravitation with respect to different distance metrics (e.g., Chebyshev, Euclidean and Hamming). The words, Part-Of-Speech (POS) tags, TF-IDF scores, variance along with Affinity Score and ranked score of the vectors were employed as important features in a supervised classification framework after a rigorous analysis. The comparative results carried out for four different classifiers e.g., NaiveBayes, J48, Decision Tree and BayesNet show satisfactory performances.