Verbs as the most “affective” words
نویسندگان
چکیده
We present a work in progress on machine learning of affect in human verbal communications. We identify semantic verb categories that capture essential properties when human communication combines spoken and written language properties. Information Extraction methods then are used to construct verb-based features that represent texts in machine learning experiments. Our empirical results show that verbs can provide a reliable accuracy in learning affect.
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