This paper presents a headline emotion classification approach based on frequency and co-occurrence information collected from the World Wide Web. The content words of a headline (nouns, verbs, adverbs and adjectives) are extracted in order to form different bag of word pairs with the joy, disgust, fear, anger, sadness and surprise emotions. For each pair, we compute the Mutual Information Score which is obtained from the web occurrences of an emotion and the content words. Our approach is based on the hypothesis that group of words which co-occur together across many documents with a given emotion are highly probable to express the same emotion.
REF extract part of speech (POS) from headlines in order to create different bag of words pairs with six emotions and compute for each pair the Mutual Information Score.
UA-ZBSA: A Headline Emotion Classification through Web Information
Fourth International Workshop on Semantic Evaluations (SemEval-2007)
Computer Science