Combining Qualitative and Computational Approaches for Literary Analysis of Finnish Novels

TL;DR

This paper explores how computational emotion analysis, combined with traditional literary methods, can enhance understanding of Finnish novels by mapping emotional spaces and analyzing large text corpora.

Contribution

It introduces a robust computational affect analysis method using a curated emotion lexicon and word embeddings tailored to Finnish literature, bridging qualitative and quantitative approaches.

Findings

Emotion arcs for 975 Finnish novels generated

Computational analysis supports traditional affect studies

Guidelines provided for replicating emotion analysis

Abstract

What can we learn from the classics of Finnish literature by using computational emotion analysis? This article tries to answer this question by examining how computational methods of sentiment analysis can be used in the study of literary works in conjunction with a qualitative or more 'traditional' approach to literature and affect. We present and develop a simple but robust 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 out the semantic emotional spaces of seminal works of Finnish literature. We focus our qualitative analysis on selected case studies: four works by Juhani Aho, Minna Canth, Maria Jotuni, and F. E. Sillanp\"a\"a, but 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 allowing for large-scale comparison between, for example, genres or national canons.