以大數據分析影響唐詩流通度之因素

Abstract

本研究旨在探討唐詩在流通上的特性與原因，期望能為唐詩詩學研究提供新的研究方向。本文以《唐詩排行榜》所建立的資料作為出發點，並以主成分分析與因子分析為主要的分析方法，萃取出唐詩在流傳上的特性及因素，探討古人與今人在詩文閱覽偏好的不同，並進一步利用詞嵌入法探討詩文內容相似度與主成分分析及因子分析之結果在排序上是否一致。\n經過對唐詩排行榜數據的研究，本文發覺主成分分析總結出以下兩項特性：1. 時代性差異 2. 詩文收錄完整性，其中時代性差異顯示『每一個時代的前理解不同，審美標準自然有明顯落差，因而造成古今閱眾對於詩文的欣賞與偏好有一定程度的差異』；而詩文收錄完整性指的是『隨著編纂需求的不同，詩作在流傳上可分為 1. 完整詩文 2. 片段名句 兩種類型』。\n而因子分析則總結出兩個影響唐詩流通的原因：1. 歷史性強度 2. 詩學經典性，其中歷史性強度所代表的是『古今閱眾在詩文內容的喜好上，深受詩文內容的歷史背景所影響』；而詩學經典性則顯示『從詩學學術領域的角度出發，可區分詩文是否為一派之經典』\n利用詞嵌入法進行詩文文本的相似性研究，發現第一主成分時代性差異、第一因子詩學經典性以及第二因子歷史性強度之結果與其分別對應之詩文相似度排序具有顯著的一致性。

This study aims to explore the characteristics of the popularity of Tang poetry, and hopes to provide new research direction for Tang poetry. First, we use multivariate statistical methods, which include principal component analysis and factor analysis, to analyze the data given by the book Ranking on Tang Poems. Based on the results of analysis, we extract the characteristics of the popularity of Tang poetry, and compare modern with ancient preferences of reading. Finally, we use word embedding techniques to further analyze the suitability of the results extracted by principal component analysis and factor analysis.\n\nAfter analyzing the data given by the Ranking on Tang Poems, principal component analysis suggests the following two characteristics: time difference and poem integrity. “Time difference” refers to “Having its own pre-understanding, each era has its own aesthetic standard, which makes some differences of poetic appreciation between ancient and modern readers”. “Poem integrity” refers to “A poem is selected either in a complete form or in a partial form according to the editing requirements.”\n\nBased on factor analysis, we sum up two factors that may influence the popularity of Tang poetry: history related strength and poetic classicism. The “history related strength” refers to “The poem preferences of ancient and modern readers may be influenced by the history related strength of the poem.” The “poetic classicism” indicates that “Poem can be considered to lead a school of thoughts from the academic perspective.”\n\nUsing word embedding techniques to study the textual similarity of poems, we find that each of first principal component and two factors has a significant rank correlation with the textual similarity of the top ranking poems based on its corresponding principal component or factor.