Analyzing the Impact of Lexicon Based Features for Emotion Classification
Keywords:
Emotion Classification, Emotion, Lexicon, Lexicon FeaturesAbstract
Emotions are psychological states that are frequently represented through actions, words or text. Emotion analysis is a method for deciphering a text to identify the feelings conveyed within it. Identification of emotion(s) contained in music lyrics is a complex process. The emotion model plays a key role in the design of emotion identification algorithms. Several text features are defined and used with machine learning algorithms for labelling lyrics based on emotion. Most of these features are defined following natural language processing concepts. Emotion lexicons play an important role in mapping words that appear in lyrics with discrete and continuous emotions. In this work, we analyze the impact of features derived from lexicons in identifying the underlying emotion of lyrics. Experiments are carried out with emotion-annotated datasets and different lexicons. Classification models are built with the lexicon features. The results obtained highlight the impact of Lexicon based features on classification accuracy. For the design of robust and efficient emotion classifier, the lexicon features need to be combined with other text based features.
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