A data science and machine learning approach to continuous analysis of Shakespeare's plays
A data science and machine learning approach to continuous analysis of Shakespeare's plays
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DOI：
10.46298/jdmdh.10829
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发表时间：
2023-01
期刊：
影响因子：
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通讯作者：
Charles F. Swisher;L. Shamir
中科院分区：
文献类型：
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作者：
Charles F. Swisher;L. Shamir
定量文本分析方法的可用性提供了新的方法来分析文献的方式，是在前信息时代不可用。在这里，我们将全面的机器学习分析应用于威廉·莎士比亚的作品。分析表明，随着时间的推移，写作风格发生了明显的变化，其中最显着的变化是句子长度，形容词和副词的频率以及文本中表达的情感。应用机器学习对戏剧的年份进行风格预测，显示实际和预测年份之间的皮尔逊相关系数为0.71，这表明定量测量反映的莎士比亚的写作风格随着时间的推移而变化。此外，它表明，一些戏剧的文体更类似于戏剧之前或之后写的一年，他们写的。例如，《罗密欧与朱丽叶》的创作日期是1596年，但在风格上与莎士比亚1600年后写的戏剧更相似。分析的源代码可供免费下载。
The availability of quantitative text analysis methods has provided new ways of analyzing literature in a manner that was not available in the pre-information era. Here we apply comprehensive machine learning analysis to the work of William Shakespeare. The analysis shows clear changes in the style of writing over time, with the most significant changes in the sentence length, frequency of adjectives and adverbs, and the sentiments expressed in the text. Applying machine learning to make a stylometric prediction of the year of the play shows a Pearson correlation of 0.71 between the actual and predicted year, indicating that Shakespeare's writing style as reflected by the quantitative measurements changed over time. Additionally, it shows that the stylometrics of some of the plays is more similar to plays written either before or after the year they were written. For instance, Romeo and Juliet is dated 1596, but is more similar in stylometrics to plays written by Shakespeare after 1600. The source code for the analysis is available for free download.