Moods in Book Reviews: Text Mining Approach. Issue 1 (13th October 2021)

• Record Type: Journal Article
• Authors: Cho, Hyerim; Adkins, Denice; Bossaller, Jenny; Moulaison‐Sandy, Heather
• Is Part Of: Proceedings of the Association for Information Science and Technology. Volume 58:Issue 1(2021)
• Page Start: 415
• Page End: 419
• Publication Date: 2021-10-13
• Subjects: affective terms -- Book reviews -- emotion -- mood -- text mining -- tone; Information science -- Congresses; Information technology -- Congresses; Information science; Information technology; Conference papers and proceedings; 020
• DOI: 10.1002/pra2.470
• Languages: English
• ISSNs: 2373-9231

Abstract

Abstract: Spiteri and Pecoskie (2018) proposed a taxonomy of terms to describe emotion and tone in novels. We tested those terms against 5, 144 full‐text book reviews from the New York Times Book Review to discover whether the proposed terms were used in published reviews to describe books, and of those terms used, which were most used. The objective of this study is to explore whether emotional contents can be identified by a text mining approach without investing too much time and efforts of information professionals. Findings demonstrate that the terms chosen by Spiteri and Pecoskie are used in professional book reviews, though some may be used in multiple ways, rather than only related to emotional content. Results of this work contribute to a larger scale project of testing machine models of identifying emotional content in books and ultimately being able to create automated media recommendation systems that include emotion as an identifier.