在R中进行文本挖掘:删除每个文档的第一句话

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英文:

Text mining in R: delete first sentence of each document

问题

case_number text
1 今天是个好天气。阳光明媚。
2 今天天气很糟糕。下雨了。

所以结果应该如下所示

case_number text
1 阳光明媚。
2 下雨了。

这是示例数据集:

case_number <- c(1, 2)

text <- c("今天是个好天气。阳光明媚。",
          "今天天气很糟糕。下雨了。")

data <- data.frame(case_number, text)
英文:

I have several documents and do not need the first sentence of each document.
I could not find a solution so far.

Here is an example. The structure of the data looks like this

case_number text
1 Today is a good day. It is sunny.
2 Today is a bad day. It is rainy.

So the results should look like this

case_number text
1 It is sunny.
2 It is rainy.

Here is the example dataset:

case_number &lt;- c(1, 2)

text &lt;- c(&quot;Today is a good day. It is sunny.&quot;,
          &quot;Today is a bad day. It is rainy.&quot;)

data &lt;- data.frame(case_number, text)

答案1

得分: 1

如果有可能句子中包含一些标点符号(例如缩写或数字),而且你已经在使用一些文本挖掘库,那么让它处理标记化是完全有道理的。

使用 {tidytext}

library(dplyr)
library(tidytext)

# 带有标点符号的第一个句子示例
data &lt;- data.frame(case_number = c(1, 2),
                   text = c(&quot;Today is a good day, above avg. for sure, by 5.1 points. It is sunny.&quot;,
                            &quot;Today is a bad day. It is rainy.&quot;))
# 将文本标记化为句子,将标记转换为小写是可选的
data %&gt;% 
  unnest_sentences(s, text)
#&gt;   case_number                                                        s
#&gt; 1           1 today is a good day, above avg. for sure, by 5.1 points.
#&gt; 2           1                                             it is sunny.
#&gt; 3           2                                      today is a bad day.
#&gt; 4           2                                             it is rainy.

# 删除每个 case_number 组的第一个记录
data %&gt;% 
  unnest_sentences(s, text) %&gt;% 
  filter(row_number() &gt; 1, .by = case_number)
#&gt;   case_number            s
#&gt; 1           1 it is sunny.
#&gt; 2           2 it is rainy.

创建于 2023-08-10,使用 reprex v2.0.2

英文:

If there's a chance that sentences might include some punctuation (e.g. abbreviations or numerics), and you are using some text mining library anyway, it makes perfect sense to let it handle tokenization.

With {tidytext} :

library(dplyr)
library(tidytext)

# exmple with punctuation in 1st sentence
data &lt;- data.frame(case_number = c(1, 2),
                   text = c(&quot;Today is a good day, above avg. for sure, by 5.1 points. It is sunny.&quot;,
                            &quot;Today is a bad day. It is rainy.&quot;))
# tokenize to sentences, converting tokens to lowercase is optional
data %&gt;% 
  unnest_sentences(s, text)
#&gt;   case_number                                                        s
#&gt; 1           1 today is a good day, above avg. for sure, by 5.1 points.
#&gt; 2           1                                             it is sunny.
#&gt; 3           2                                      today is a bad day.
#&gt; 4           2                                             it is rainy.

# drop 1st record of every case_number group
data %&gt;% 
  unnest_sentences(s, text) %&gt;% 
  filter(row_number() &gt; 1, .by = case_number)
#&gt;   case_number            s
#&gt; 1           1 it is sunny.
#&gt; 2           2 it is rainy.

<sup>Created on 2023-08-10 with reprex v2.0.2</sup>

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  • 本文由 发表于 2023年8月10日 15:05:52
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