Automatic Affect Recognition Using Natural Language Processing Techniques and Manually Built Affect Lexicon

In this paper, we present preliminary work on recognizing affect from a Korean textual document by using a manually built affect lexicon and adopting natural language processing tools. A manually built affect lexicon is constructed in order to be able to detect various emotional expressions, and its entries consist of emotion vectors. The natural language processing tools analyze an input document to enhance the accuracy of our affect recognizer. The performance of our affect recognizer is evaluated through automatic classification of song lyrics according to moods.