QUANTITATIVE ANALYSIS OF QUALITATIVE DATA: USE OF TEXT MINING TECHNIQUES FOR MUSIC THERAPY CLINICS

Abstract

Text mining is a data mining process that aims to extract useful information from unstructured or semi-structured data sets, such as emails, HTML files or transcripts. This study proposes the use of text mining techniques to assess verbal communication in music therapy sessions. For this purpose, sessions carried out by the project Effects of Music Therapy Interventions on the Quality of Life of black women were transcribed. Word cloud, topical modeling and sentiment analysis techniques were employed in this research. Topic modeling identified four groupings of words called “Black Women”, “Reflections”, “Music Therapy” and “Pain”. In turn, sentiment analysis identified a broad distribution of sentiments, with a predominance of neutral or positive sentences, heavily biased to the right and concentrated around the mean, with few extremely negative or positive sentences. These data suggest that the sessions dealt with sentimental issues related to the participating black women and promoted well-being for this population. According to the participants' reports in response to a questionnaire at the end of music therapy sessions, the results of text mining are in line with the participants' perception. In this sense, text mining is a promising technique for understanding verbal communication in music therapy sessions, and can be used in future research to investigate, with more evidence, these and other possibilities for the clinical practice of music therapy.