用于文本聚类分析的tf-idf

Question

我想对数据帧中的列df['Texts']中包含的小文本进行分组。下面是一个要分析的句子示例：

    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(通过遵循社区中之前的一些问题)：

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，由于我考虑的是来自dataframe的列，所以我不知道如何应用上述函数。你能帮我拿一下吗？

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)

你只需要用你的df替换all_text即可。最好先构建一个管道，然后同时应用向量化器和Kmeans。

此外，为了获得更精确的结果，对文本进行更多的预处理也不是一个坏主意。此外，我不认为降低文本是一个好主意，因为你很自然地删除了写作风格的一个好特征(如果我们认为你想要找到作者或将作者分配到一个组中)，但为了获得句子的情感，是的，最好是降低。