August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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44
｜
第
1
章
1.10
小结
在本章中，我们介绍了文本数据分析开头的一些工作。准备文本和分词的过程应该
保持简洁，以快速获得结果。我们将在第
4
章中介绍更复杂的方法，并讨论不同方
法的优缺点。
数据探索不仅可以让我们对数据有初步的了解，而且还有助于我们建立对数据的信
心。你应该记住：遇到任何词语突然出现异常，都应该刨根问底，挖掘根本原因。
KWIC
分析是搜索这类词语的一款好工具。
在内容分析方面，我们介绍了一些词频分析的案例。词条的加权既可以基于单个词
条，也可以基于词条频率与逆向文档频率（
TF-IDF
）的组合。我们将在第
5
章中详
细介绍这些概念，因为
TF-IDF
加权是面向机器学习的文档向量化的标准方法。
还有很多文本分析的方面，本章未能逐一介绍，其中包括：
•
与作者相关的信息可以帮助你找出有影响力的作家（如果这是项目的目标之一
的话），你可以通过活动、社交评分、写作风格等来甄别作家。
•
有时，你可以通过可读性来比较同一个主题的作者或不同的语料库。
textacy
（
https://oreil.ly/FRZJb
）提供了一个名叫
textstats
的函数，只需遍历一次文
本就可以计算出可读性评分以及其他统计信息。
• Jason Kessler
的
Scattertext
（
https://oreil.ly/R6Aw8
）库是一个很有趣的工具，
能够识别和可视化类别（例如政党）之间不同的词条。
•
除了普通的
Python
外，你还可以使用交互式可视化工具进行数据分析。微软的
PowerBI
有一个很不错的词云插件，而且还有许多其他选项可以生成交互式图表。 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.