Forum Discussion
Python word clustering
Hello,
im currently facing a Python clustering problem. I would like to extract from a column (say "comment") from the table "rawdata" the individual comments from customers, take out stop words and then cluster by similar sounding terms. Similar sounding because umlauts like ä,ö... are not always saved/ displayed correctly.
Unfortunately, i cant get a visual to work here. Can someone help me here?
Code I tried to use:
# The following code for creating a data frame and removing duplicate rows is always executed and serves as a preamble to your script:
# dataset = pandas.DataFrame(Comments)
# dataset = dataset.drop_duplicates()
# Fügen oder geben Sie hier Ihren Skriptcode ein:
import pandas as pd
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
df = pd.read_excel("C:\Users\Data.xlsx")
def extract_entities(text) :
stop_words = set(stopwords.words('german'))
word_tokens = word_tokenize(text)
filtered_tokens = [word.lower() for word in word_tokens if word.lower() not in stop_words and word.isalpha()]
tagged = nltk.pos_tag(filtered_tokens, lang='de')
entities = nltk.chunk.ne_chunk(tagged, binary=True)
return entities
df['entities'] = df['Disp_Comments'].apply(extract_entities)
df.groupby('entities')['Disp_Comments'].count()
Data sometimes looks like this:
| ID | Comment |
| 1 | Kündigung rückgängig machen |
| 2 | Invoice |
1 Reply
Your Python visual needs to plot something to the default renderer. What are you planning to display once you are done?
