An Automated Approach to Classifying Music Reviews According to Their Descriptive Content

Abstract

Detailed analysis of the content and writing style of music reviews has not yet been sufficiently conducted, despite the potential for industrial and academic development. In this research, we tackle the task of automatically classifying each sentence of a music review by computer according to its content. We also developed a classification scheme for this purpose, and constructed an annotated corpus based on the scheme. Each sentence of a review may consist of parts describing the background of the composition, the personal information of the player, the characteristics of the performance, or the author's impression from the performance. In order to create a classifier that can automatically perform such a classification, we first designed an annotation scheme that enables clear classification by humans. Based on this scheme, we manually annotated the review text data collected from a music review website, and constructed a dataset for training and evaluation of machine learning classifier. These results were evaluated by the degree of annotation agreement between annotators and the accuracy (F-measure) of the classifier.