Reputation: 25
Here is my code
URL to CSV file: https://github.com/eugeneketeni/web-mining-final-project/blob/master/Test_file.csv
import pandas as pd
data = pd.read_csv("https://raw.githubusercontent.com/eugeneketeni/web-
mining-final-project/master/Test_file.csv")
import nltk
from nltk import word_tokenize, sent_tokenize
data['text'] = data.loc[:, 'text'].astype(str)
text = data.loc[:, "text"].astype(str)
tokenizer = [word_tokenize(text[i]) for i in range(len(text))]
print(tokenizer)
filtered_sentence = []
from nltk.corpus import stopwords
stopwords = set(stopwords.words('english'))
filtered_sentence = [w for w in tokenizer if not w in stopwords]
print(filtered_sentence)
My tokenizer works but when I try to remove the default stopwords, I keep getting "unhashable type: 'list'" error. I am not sure what really going on. I would appreciate any help. Thanks.
Upvotes: 1
Views: 888
Reputation: 122158
from nltk import word_tokenize
from nltk.corpus import stopwords
import pandas as pd
stoplist = set(stopwords.words('english'))
data = pd.read_csv("Test_file.csv")
data['filtered_text'] = data['text'].astype(str).apply(lambda line: [token for token in word_tokenize(line) if token not in stoplist])
Please see Why is my NLTK function slow when processing the DataFrame? for more detailed explanation on:
For better, twitter text processing
pip3 install -U nltk[twitter]
Then use this:
from nltk.corpus import stopwords
from nltk.tokenize import TweetTokenizer
import pandas as pd
word_tokenize = TweetTokenizer().tokenize
stoplist = set(stopwords.words('english'))
data = pd.read_csv("Test_file.csv")
data['filtered_text'] = data['text'].astype(str).apply(lambda line: [token for token in word_tokenize(line) if token not in stoplist])
Upvotes: 1