用于文本聚类分析的tfidf

Question

我想将数据帧中的小文本分组到一列df['Texts']中。 要分析的句子示例如下：

    Texts

  1 Donald Trump, Donald Trump news, Trump bleach, Trump injected bleach, bleach coronavirus.
  2 Thank you Janey.......laughing so much at this........you have saved my sanity in these mad times. Only bleach Trump is using is on his heed 🤣
  3 His more uncharitable critics said Trump had suggested that Americans drink bleach. Trump responded that he was being sarcastic.
  4 Outcry after Trump suggests injecting disinfectant as treatment.
  5 Trump Suggested 'Injecting' Disinfectant to Cure Coronavirus?
  6 The study also showed that bleach and isopropyl alcohol killed the virus in saliva or respiratory fluids in a matter of minutes.

因为我知道TF-IDF对集群很有用，所以我一直在使用以下代码行（通过遵循社区中以前的一些问题）：

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
import re
import string

def preprocessing(line):
    line = line.lower()
    line = re.sub(r"[{}]".format(string.punctuation), " ", line)
    return line

tfidf_vectorizer = TfidfVectorizer(preprocessor=preprocessing)
tfidf = tfidf_vectorizer.fit_transform(all_text)

kmeans = KMeans(n_clusters=2).fit(tfidf) # the number of clusters could be manually changed

但是，由于我考虑的是数据帧中的列，所以我不知道如何应用上述函数。 你能帮我吗

Answer 1

def preprocessing(line):
    line = line.lower()
    line = re.sub(r"[{}]".format(string.punctuation), " ", line)
    return line

tfidf_vectorizer = TfidfVectorizer(preprocessor=preprocessing)
tfidf = tfidf_vectorizer.fit_transform(df['Texts'])

kmeans = KMeans(n_clusters=2).fit(tfidf)

您只需要用df替换所有的_文本。最好先构建一个管道，然后同时应用矢量器和Kmeans

另外，为了得到更精确的结果，对文本进行更多的预处理从来都不是一个坏主意。此外，我不认为降低文本是一个好主意，因为你自然删除一个良好的特点，为写作风格（如果我们认为你想找到作者或分配作者到一个组），但为了获得感情的句子是，最好是降低。