使用全文内容来描述和识别销书:对20世纪初文学的一项研究
Giovana D da Silva1, Filipi N Silva2, Henrique F de Arruda3
1Institute of Mathematics and Computer Science - USP, São Carlos, SP, Brazil.
PloS one
|April 26, 2024
概括
预测书籍销量是一项挑战. 这项研究使用了全书内容和机器学习来分析销书,达到75%的准确性,但突出了精确的文学成功预测的困难.
科学领域:
- 计算语言学计算语言学
- 文学研究是文学研究.
- 数据科学是数据科学.
背景情况:
- 评估艺术的接收随着时间的推移至关重要.
- 预测文学销书仍然是一个复杂的挑战.
研究的目的:
- 使用全文内容评估书籍成功的可预测性.
- 探索用于文学分析的可视化和分类方法.
主要方法:
- 利用SemAxis和线性差别分析进行数据可视化.
- 采用各种分类器,包括用袋词表示的逻辑回归.
- 分析了1895-1923年的销书和非销书数据集.
主要成果:
- 最好的模型 (带有逻辑回归的词袋) 实现了0.75的平均准确性.
- 离开一个和十倍的交叉验证都产生了类似的结果.
- 准确预测书籍的成功仍然很困难,即使使用全文分析.
结论:
- 全文分析和机器学习提供了洞察力,但不能保证精确的销书预测.
- 这项研究强调了预测文学成功的固有困难.
- 这些发现有助于理解影响文学作品相对成功的因素.
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