Reputation: 111
i need to find the opinion of certain reviews given in websites. i am using sentiwordnet for this. i first send the file containing all the reviews to POS Tagger.
tokens=nltk.word_tokenize(line) #tokenization for line in file
tagged=nltk.pos_tag(tokens) #for POSTagging
print tagged
Is there any other accurate way of tokenizing which considers not good as 1 word other than considering it as 2 separate words.
Now i have to give postive and negative score to the tokenized words and then calculate the total score. Is there any function in sentiwordnet for this. please help.
Upvotes: 2
Views: 4767
Reputation: 1057
See First Extract Adverbs and Adjectives from review for example:
import nltk
from nltk.tokenize import sent_tokenize, word_tokenize
import csv
para = "What can I say about this place. The staff of the restaurant is nice and the eggplant is not bad. Apart from that, very uninspired food, lack of atmosphere and too expensive. I am a staunch vegetarian and was sorely dissapointed with the veggie options on the menu. Will be the last time I visit, I recommend others to avoid"
sentense = word_tokenize(para)
word_features = []
for i,j in nltk.pos_tag(sentense):
if j in ['JJ', 'JJR', 'JJS', 'RB', 'RBR', 'RBS']:
word_features.append(i)
rating = 0
for i in word_features:
with open('words.txt', 'rt') as f:
reader = csv.reader(f, delimiter=',')
for row in reader:
if i == row[0]:
print i, row[1]
if row[1] == 'pos':
rating = rating + 1
elif row[1] == 'neg':
rating = rating - 1
print rating
Now you must have a external csv file in which you should have positive and negative words
like : wrinkle,neg wrinkled,neg wrinkles,neg masterfully,pos masterpiece,pos masterpieces,pos
Working of the above script as follows:
1 . read sentence 2 . extract adverb and adjectives 3 . compare to CVS for positive and negative words 4 . and then rate the sentence
Result of above script is :
nice pos
bad neg
expensive neg
sorely neg
-2
change result as per your need. and sorry for my english :P
Upvotes: 3