CountVectorizer 构建字典以删除多余的单词

Question

我在 pandas 列中有一个句子列表：

sentence
I am writing on Whosebug because I cannot find a solution to my problem.
I am writing on Whosebug. 
I need to show some code. 
Please see the code below

我想运行通过他们进行一些文本挖掘和分析，比如获取词频。 为此，我正在使用这种方法：

from sklearn.feature_extraction.text import CountVectorizer
# list of text documents
text = ["I am writing on Whosebug because I cannot find a solution to my problem."]
vectorizer = CountVectorizer()
# tokenize and build vocab
vectorizer.fit(text)

如何将它应用到我的专栏中，在构建词汇表后删除多余的停用词？

Answer 1

您可以在 CountVectorizer 中使用 stop_words 参数，这将负责删除停用词：

from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
text = ["I am writing on Whosebug because I cannot find a solution to my problem."]
stopwords = stopwords.words("english") # you may add or define your stopwords here
vectorizer = CountVectorizer(stop_words=stopwords)
vectorizer.fit_transform(text)

如果您想在 pandas 数据帧内进行所有预处理：

from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
text = ["I am writing on Whosebug because I cannot find a solution to my problem.", "I am writing on Whosebug."]
df = pd.DataFrame({"text": text})
stopwords = stopwords.words("english") # you may add or define your stopwords here
vectorizer = CountVectorizer(stop_words=stopwords)
df["counts"] = vectorizer.fit_transform(df["text"]).todense().tolist()
df
                                                text              counts
0  I am writing on Whosebug because I cannot...  [1, 1, 1, 1, 1, 1]
1                     I am writing on Whosebug.  [0, 0, 0, 0, 1, 1]

在这两种情况下，您的词汇表都删除了停用词：

print(vectorizer.vocabulary_)
{'writing': 5, 'Whosebug': 4, 'cannot': 0, 'find': 1, 'solution': 3, 'problem': 2}