August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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42
｜
第
1
章
图 1-8：所选单词每年的频率
请注意，
1980
年代出现的“
nuclear
”（核武器）高峰表明了那场军备竞赛，而
2001
年则出现了“
terrorism
”（恐怖主义）的高峰。值得注意的是，主题“
climate
”（气候）
在
1970
年代和
1980
年代就受到了关注。这是真的吗？查看
KWIC
分析（请参见
1.7
节），你就会发现在过去的几十年中，“
climate
”（气候）一词几乎仅作为一种象
征意义出现。
1.9.2
创建词频热图
假设我们想分析冷战、恐怖主义以及气候变化等全球危机的历史发展。我们可以选
择一个重要的单词，并像上一个示例那样将其时间线绘制成折线图。但是，如果折
线图中的折线超过
4
～
5
条，那么就很混乱了。另一种没有这种限制的可视化方法
是采用
Seaborn
库提供的热图。下面，我们将更多关键字添加到过滤器中，并将结
果绘制成热图（见图
1-9
）。
keywords = ['terrorism', 'terrorist', 'nuclear', 'war', 'oil',
'syria', 'syrian', 'refugees', 'migration', 'peacekeeping',
'humanitarian', 'climate', 'change', 'sustainable', 'sdgs']
freq_df = count_keywords_by(df, by='year', keywords=keywords)
# compute relative ...
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