Emotions for artists: Intregrating two textual analysis techniques in a qualitative perspective

Abstract

This study aimed to show, by empirical evidence, that using different techniques of data analysis can contribute to the production of complementary knowledge about complex phenomena, such as emotions. The article discusses the results derived from using two textual analysis techniques and their articulation. Its main contribution is methodological, specifically in qualitative analysis supported by software. The study included 517 artists working in various artistic sectors, such as music and theater. ALCESTE and ATLAS.ti were used in the analysis. Results suggest convergences or complementarities between these two techniques. While ATLAS.ti allows for a dialogue between data and theory, through open coding, for better alignment between categorical theoretical system and data, ALCESTE organizes data in classes or categories, through calculations of word co-occurrence, which requires a theoretical frame to give them meaning.