Exploring Reader-Generated Language to Describe Multicultural Literature

Abstract

How do readers describe multicultural fiction works? While in library and information science (LIS) we have the language of appeal factorsand genre trendsto describe works of fiction, these linguistic choices may not be used by readers to describe their own responses and reactions to works that provide cultural affirmation of one’s own culture or exposure to learning different cultures. In this research, text mining processes are employed to harvest reader-generated book reviews and subsequently analyze the words readers use to describe award-winning multicultural fiction on the retailer site Amazon.com. Our goal with this study is to provide LIS professionals an insight into readers’ perspectives related to multicultural fiction. We describe our methodology of engaging in topic modeling as described by Jockers and Mimno (2013) as applied to multicultural fiction reviews. First, we explore the construction and processing of a corpus of reader reviews of multicultural fiction titles, then we model topics using a topic modeling toolkit to generate topics from these reviews. Through this analysis, we determine consistent terms used to describe multicultural fiction that can be used to indicate common reader experience and identify topics. Closing discussion reflects on whether librarians can use text mining of reader reviews to enhance their reader advisory services for readers seeking books that represent multiple and/or diverse cultures.

Keywords

• Amazon reviews
• appeal factors
• multicultural fiction
• multicultural literature
• topic modeling