Combining Qualitative and Computational Approaches for Literary Analysis of Finnish Novels

Abstract

What can we learn from the classics of Finnish literature by using computational emotion analysis? This article addresses this question by examining how computational methods of sentiment analysis can be used for studying literary works in conjunction with a qualitative or more “traditional” approach to literature and affect. We present and develop a computational approach of affect analysis that uses a carefully curated emotion lexicon adapted to Finnish turn-of-the-century literary texts combined with word embeddings to map the semantic emotional spaces of seminal works of Finnish literature. We focus the qualitative analysis on case studies: four works by Juhani Aho, Minna Canth, Maria Jotuni and F. E. Sillanpää; we provide emotion arcs for a total of 975 Finnish novels. We argue that a computational analysis of a text’s lexicon can be valuable in evaluating the large distribution of the emotional valence in a text and provide guidelines to help other researchers replicate our findings. We show that computational approaches have a place in traditional studies on affect in literature as a support tool for close reading–based analyses, but also allow for large-scale comparison between genres or national canons.