Artificial Intelligence Applications in Literary Works: Emotion Extraction and Classification of Mohsin Hamid's Moth Smoke

Introduction

The British Pakistani writer, Mohsin Hamid’s debut novel Moth Smoke (2000) has garnered conflicting responses from readers across the globe. The novel tells the story of a young man named Darashikoh Shezad or Daru who plunges into a life of drugs and crime. While some have acclaimed the novel for depicting a more vivid and disturbing portrait of Pakistan than the exoticized and stereotypical images of South Asia known to most Western readers, others have criticized it for promoting a negative image of the country (Asl, 2022; Dagamsheh & Downing, 2016; Perner, 2010). Over the past few years and with the rapid advancements in social media platforms, readers around the world have used online platforms such as Twitter, Goodreads, Facebook—among many others—to share their opinions and feelings towards literary texts (Keikhosrokiani & Asl, 2022). When one explores the landing page of the novel in Goodreads—which includes rating details, number of ratings and number of reviews—it becomes evident that the story has provoked differing responses from readers. The emotional categories of readers’ responses can be classified into three basic types of positive, negative, and neutral as in sentiment feedback. This study aims to analyze the reviews of Mohsin Hamid’s debut novel Moth Smoke (2000) in Goodreads to detect the dominant emotion existing within the readers’ feedback. Understanding readers’ emotion towards the novel can help in developing a recommendation system that can suggest readers stories of their interest. The next section of this chapter focusses on the background of the study followed by the proposed method. Then results and discussion are added. Finally, the chapter is wrapped up with a conclusion.