Textblob giving memory error while using NaiveBayesAnalyzer on large dataset
0
I am opening each text file and assigning it a label -- pos or neg as per for training NaiveBayes classifier.The data set contains about 12,000 txt files. I am using TextBlob library for sentiment analysis
train = [('I dont like this movie','neg')]
path = 'C://TextDemo//senti//aclImdb//train//neg//*.txt'
for f in glob.glob(path):
with open(f, "r", encoding="UTF-8") as read_file:
for line in read_file:
train.append(((line.replace("<br />","")),'pos'))
cl = NaiveBayesClassifier(train)
python textblob
asked Nov 25 '18 at 12:59
user10646468user10646468
1
add a comment |
0
I am opening each text file and assigning it a label -- pos or neg as per for training NaiveBayes classifier.The data set contains about 12,000 txt files. I am using TextBlob library for sentiment analysis
train = [('I dont like this movie','neg')]
path = 'C://TextDemo//senti//aclImdb//train//neg//*.txt'
for f in glob.glob(path):
with open(f, "r", encoding="UTF-8") as read_file:
for line in read_file:
train.append(((line.replace("<br />","")),'pos'))
cl = NaiveBayesClassifier(train)
python textblob
asked Nov 25 '18 at 12:59
user10646468user10646468
1
You use up too much memory. So either buy more memory (and make sure you use 64 Bit Python), or train your classifier in batches.
– deets
Nov 25 '18 at 13:22
Alright! Thank you. I thought I didn't optimize my code :) I will try this of 16GB RAM.
– user10646468
Nov 30 '18 at 15:14
add a comment |
0
0
0
I am opening each text file and assigning it a label -- pos or neg as per for training NaiveBayes classifier.The data set contains about 12,000 txt files. I am using TextBlob library for sentiment analysis
train = [('I dont like this movie','neg')]
path = 'C://TextDemo//senti//aclImdb//train//neg//*.txt'
for f in glob.glob(path):
with open(f, "r", encoding="UTF-8") as read_file:
for line in read_file:
train.append(((line.replace("<br />","")),'pos'))
cl = NaiveBayesClassifier(train)
python textblob
asked Nov 25 '18 at 12:59
user10646468user10646468
1
I am opening each text file and assigning it a label -- pos or neg as per for training NaiveBayes classifier.The data set contains about 12,000 txt files. I am using TextBlob library for sentiment analysis
train = [('I dont like this movie','neg')]
path = 'C://TextDemo//senti//aclImdb//train//neg//*.txt'
for f in glob.glob(path):
with open(f, "r", encoding="UTF-8") as read_file:
for line in read_file:
train.append(((line.replace("<br />","")),'pos'))
cl = NaiveBayesClassifier(train)
python textblob
python textblob
asked Nov 25 '18 at 12:59
user10646468user10646468
1
asked Nov 25 '18 at 12:59
user10646468user10646468
1
asked Nov 25 '18 at 12:59
user10646468user10646468
1
asked Nov 25 '18 at 12:59
user10646468user10646468
1
asked Nov 25 '18 at 12:59
user10646468user10646468
1
1
You use up too much memory. So either buy more memory (and make sure you use 64 Bit Python), or train your classifier in batches.
– deets
Nov 25 '18 at 13:22
Alright! Thank you. I thought I didn't optimize my code :) I will try this of 16GB RAM.
– user10646468
Nov 30 '18 at 15:14
add a comment |
You use up too much memory. So either buy more memory (and make sure you use 64 Bit Python), or train your classifier in batches.
– deets
Nov 25 '18 at 13:22
Alright! Thank you. I thought I didn't optimize my code :) I will try this of 16GB RAM.
– user10646468
Nov 30 '18 at 15:14
You use up too much memory. So either buy more memory (and make sure you use 64 Bit Python), or train your classifier in batches.
– deets
Nov 25 '18 at 13:22
You use up too much memory. So either buy more memory (and make sure you use 64 Bit Python), or train your classifier in batches.
– deets
Nov 25 '18 at 13:22
Alright! Thank you. I thought I didn't optimize my code :) I will try this of 16GB RAM.
– user10646468
Nov 30 '18 at 15:14
Alright! Thank you. I thought I didn't optimize my code :) I will try this of 16GB RAM.
– user10646468
Nov 30 '18 at 15:14
add a comment |
0
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You use up too much memory. So either buy more memory (and make sure you use 64 Bit Python), or train your classifier in batches.
– deets
Nov 25 '18 at 13:22
Alright! Thank you. I thought I didn't optimize my code :) I will try this of 16GB RAM.
– user10646468
Nov 30 '18 at 15:14