A Computational Sentiment and Emotion Analysis of Kae Bahar’s The Good, the Bad and the Gringo

This study presented a systematic computational analysis of sentiment and emotion in Kae Bahar’s The Good, the Bad and the Gringo. The objective of the study was to identify the emotional tone (valence) of the overall sentiment in the novel, to detect the trajectories of six discrete emotions (anger, fear, sadness, happiness, disgust, and surprise), and to examine the correlation between them. The study adopted a quantitative and qualitative approach and utilized Orange Data Mining v.3.37 software for the text analysis. More specifically, the study made use of SentiArt tool and Pearson Correlation tool, which are incorporated within the software, to address its objectives. The findings demonstrated that the novel, although ends on a positive note, is noticeably oriented towards negative sentiment territory as the life of the main character goes downward recurrently. The study also found that the six discrete emotions have trajectories with different ranges, and that both positive and negative correlations exist between several emotional arcs. The novel’s affective structure, therefore, operates as a testament to endurance rather than a surrender to despair. The narrative foregrounds resilience, documenting the strained yet persistent adaptation of human consciousness to unrelenting adversity. The study contributes to the field of digital humanities methodologically as it supports the usefulness of SentiArt tool in computational literary analysis, and theoretically as it provides insights to the field from non-Western narratives. Moreover, the study contributes to the computational analysis of Kurdish literature, a domain that has often been overlooked in prior research.