Readers' responses to literature have received scant attention in computational literary studies. The rise of social media offers an opportunity to capture a segment of these responses while data-driven analysis of these responses can provide new critical insight into how people "read". Posts discussing an individual book on Goodreads, a social media platform that hosts user discussions of popular literature, are referred to as "reviews", and consist of plot summaries, opinions, quotes, or some mixture of these. Since these reviews are written by readers, computationally modeling them allows one to discover the overall non-professional discussion space about a work, including an aggregated summary of the work's plot, an implicit ranking of the importance of events, and the readers' impressions of main characters. We develop a pipeline of interlocking computational tools to extract a representation of this reader generated shared narrative model. Using a corpus of reviews of five popular novels, we discover the readers' distillation of the main storylines in a novel, their understanding of the relative importance of characters, as well as the readers' varying impressions of these characters. In so doing, we make three important contributions to the study of infinite vocabulary networks: (i) an automatically derived narrative network that includes meta-actants; (ii) a new sequencing algorithm, REV2SEQ, that generates a consensus sequence of events based on partial trajectories aggregated from the reviews; and (iii) a new "impressions" algorithm, SENT2IMP, that provides finer, non-trivial and multi-modal insight into readers' opinions of characters.
翻译:阅读者对文学的反应在计算文学研究中很少引起注意。社交媒体的兴起提供了一个机会,可以捕捉这些反应的一部分内容,而以数据驱动对这些反应的分析可以提供对人们“阅读”方式的新的批判性洞察力。在讨论《Goodreads》个人书籍的站点上,这是一个社交媒体平台,它容纳用户对流行文学的讨论,被称为“评论”,由5种流行小说的审查汇编组成,我们发现读者在小说中对主要故事的提炼,他们理解各种字符的相对重要性,以及读者对这些字符的不同印象。因此,我们从“工作情节的汇总、对事件重要性的隐含性排序以及读者对主要人物的印象”中做了三个重要的贡献。我们开发了一个连接计算工具的管道,用来表达读者对流行文献的“Goodreadbooks”的描述。我们从5种流行小说中了解到读者对主要故事的提炼,他们对这些字符的相对重要性的理解,以及读者对这些字符的不同印象。我们从“inal Qrial i ” 中做了三个重要的贡献,从“intial recalalalal commal commal commalalalalal 包括一个基于Siabalalalalalalal revialalalalalalalalalalal 网络的、产生一个基于Squalbalbalalalalbal 。