Text Mining Life Cycle for a Spatial Reading of Viet Thanh Nguyen's The Refugees (2017)
Esraa Faisal Malik, Pantea Keikhosrokiani, Moussa Pourya Asl
Abstract
Textual analysis is traditionally used by literary critics as a central methodology to interpret creative writings, however this method is significantly affected by human-error, which cause the failing to offer one correct interpretation of the text. As an example, the Vietnamese-American writer Viet Thanh Nguyen’s short story collection The Refugees (2017) has received opposing critical receptions: Whereas some critics applaud the stories for their truthful representations of the two countries, others criticize them for their biased depictions. This study aims to demonstrate how text mining can offer a more objective analysis of the representation of the two main countries in the selected stories. We propose a Big Data Analytics lifecycle that consider two empirical methods. The first method used N-grams, while the second method propose sentiment analysis using lexicon dictionary. The study revealed that text mining is useful in discovering the hidden pattern of textual data and resolving the problem of human error that occurs in performing the analytics manually.