August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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然后求出总和就能得出整体的情感。使用这项技术可以免去手动阅读每条评论并指
定情感标签的工作，我们可以依靠词典，因为它提供了专家给出的每个单词的情感
分数。在第一个案例中，我们使用刘兵词典，但是你可以自由扩展我们的方法，使
用其他词典。一般词典包含单词的几种变体，但不包含停用词，因此不需要进行标
准的预处理。只有出现在词典中的单词才会被实际打分。而这也导致了这种方法的
一个缺点，我们留到案例的最后再讨论。
11.4.1 Bing Liu lexicon
Bing Liu lexicon
（即刘兵词典）的编译方式是，将单词分成两组，一组是表达正面
情感的单词，一组是表达负面情感的单词。该词典还包含拼写错误的单词，更适合
分析从在线论坛、社交媒体以及其他此类来源中提取的文本，因此应该能够针对亚
马逊的客户评论数据给出良好的结果。
你可以通过作者的网站，下载刘兵词典的
zip
文件（
https://oreil.ly/A_O4Q
），其
中包含一组表达正面情感的单词和一组表达负面情感的单词。另外，
NLTK
库也提
供了这个词典作为语料库，可供我们下载后使用。在词典解压缩后，我们创建一个
dictionary
，用于保存词典中的单词及其相应的情感评分。下一步是为数据集中的每
一条评论生成评分。首先，我们将文本内容转换为小写；然后使用
NLTK
软件包中
的
word_tokenize
函数，将句子拆分成单词，并检查我们的
dictionary
中是否包含
这个单词。如果包含，则我们将单词相应的情感评分加到情感总分中。最后，我们 ...
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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.