CISUC

Keyword-Based Approach for Lyrics Emotion Variation Detection

Authors

Abstract

This research addresses the role of the lyrics in the context of music emotion variation detection. To accomplish this task we create a system to detect the predominant emotion expressed by each sentence (verse) of the lyrics. The system employs Russell’s emotion model and contains 4 sets of emotions associated to each quadrant. To detect the predominant emotion in each verse, we propose a novel keyword-based approach, which receives a sentence (verse) and classifies it in the appropriate quadrant. To tune the system parameters, we created a 129-sentence training dataset from 68 songs. To validate our system, we created a separate ground-truth containing 239 sentences (verses) from 44 songs annotated manually with an average of 7 annotations per sentence. The system attains 67.4% F-Measure score.

Subject

Music Emotion Recognition, Music Information Retrieval, Natural Language Processing

Related Project

MOODetector: A System for Mood-based Classification and Retrieval of Audio Music

Conference

8th International Conference on Knowledge Discovery and Information Retrieval – KDIR’2016, October 2016

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