def search_and_return(search_string, subreddit, classifier):
# I may need to set this as a global
# For now, I will call one every single time
# *NOT SCALABLE*
r = praw.Reddit('search application')
# search
search_results = r.search(search_string,
subreddit = subreddit,
sort = True)
for i in range(0,24):
comments = []
post = search_results.next()
for comment in post.comments:
try:
comment_str = str(comment.body.encode('utf-8'))
comments.append(comment_str)
comments.append(comment.score)
except AttributeError:
pass
score = 0
for i in range(0, len(comments), 2):
for comment in comments[i].split('\n'):
if any(i in str(comment) for i in search_string.lower().split(' ')):
#print comment
sentiment = classifier.classify(word_feats(comment))
if sentiment == 'neg':
score += -1 * int(comments[i+1])
else:
score += 1 * int(comments[i+1])
if score > 10:
return "POSITIVE " + str(score)
elif score < -10:
return "NEGATIVE " + str(score)
else:
return "NEUTRAL " + str(score)
# NBclassifier = classifier()
# print search_and_return('lebron','nba',NBclassifier)
# print search_and_return('trump' , 'politics', NBclassifier)
# print search_and_return('peyton manning', 'nfl', NBclassifier )
# print search_and_return('monty williams', 'nba', NBclassifier)
# print search_and_return('frank underwood', 'houseofcards', NBclassifier)
def search_and_return(search_string, subreddit, classifier):
# I may need to set this as a global
# For now, I will call one every single time
# *NOT SCALABLE*
r = praw.Reddit('search application')
# search
search_results = r.search(search_string, subreddit=subreddit, sort=True)
for i in range(0, 24):
comments = []
post = search_results.next()
for comment in post.comments:
try:
comment_str = str(comment.body.encode('utf-8'))
comments.append(comment_str)
comments.append(comment.score)
except AttributeError:
pass
score = 0
for i in range(0, len(comments), 2):
for comment in comments[i].split('\n'):
if any(i in str(comment)
for i in search_string.lower().split(' ')):
#print comment
sentiment = classifier.classify(word_feats(comment))
if sentiment == 'neg':
score += -1 * int(comments[i + 1])
else:
score += 1 * int(comments[i + 1])
if score > 10:
return "POSITIVE " + str(score)
elif score < -10:
return "NEGATIVE " + str(score)
else:
return "NEUTRAL " + str(score)
# NBclassifier = classifier()
# print search_and_return('lebron','nba',NBclassifier)
# print search_and_return('trump' , 'politics', NBclassifier)
# print search_and_return('peyton manning', 'nfl', NBclassifier )
# print search_and_return('monty williams', 'nba', NBclassifier)
# print search_and_return('frank underwood', 'houseofcards', NBclassifier)
def search_and_return(search_string, subreddit, classifier):
# I may need to set this as a global
# For now, I will call one every single time
# *NOT SCALABLE*
r = praw.Reddit('search application')
now = datetime.datetime.now()
# search for string in subreddit
search_results = r.search(search_string,
subreddit = subreddit,
sort = True)
# there are 25 posts and then a next option. we can just use the limit
#for i in xrange(search_results.__sizeof__()):
for i in range(0,24):
score = 0
comments = []
post = search_results.next()
for comment in post.comments:
try:
comment_str = str(comment.body.encode('utf-8'))
comments.append(comment_str)
comments.append(comment.score)
except AttributeError:
pass
for i in range(0, len(comments), 2):
for comment in comments[i].split('\n'):
if any(i in str(comment) for i in search_string.lower().split(' ')):
#print comment
sentiment = classifier.classify(word_feats(comment))
if sentiment == 'neg':
score += -1 * int(comments[i+1])
else:
score += 1 * int(comments[i+1])
if score > 10:
result = str(now) + " POSITIVE " + str(score)
elif score < -10:
result = str(now) + " NEGATIVE " + str(score)
else:
result = str(now) + " NEUTRAL " + str(score)
#return result, max_post_comment, pos_score, max_neg_comment, neg_score
return result
def sentiment_analyse():
import sentiment
from sentiment import runtweets
from sentiment import classifier
from sentiment import *
if len(request.vars)!=0:
user_input=request.vars
runtweets.append(user_input.input)
poscount = 0
negcount = 0
for tweett in runtweets:
valued = classifier.classify(extract_features(tweett.split()))
print (valued)
if valued == 'negative':
negcount = negcount + 1
else:
poscount = poscount + 1
print ('Positive count: %s \nNegative count: %s' % (poscount,negcount))
def search_and_return(search_string, subreddit, classifier):
# I may need to set this as a global
# For now, I will call one every single time
# *NOT SCALABLE*
r = praw.Reddit('search application')
now = datetime.datetime.now()
# search for string in subreddit
search_results = r.search(search_string, subreddit=subreddit, sort=True)
# there are 25 posts and then a next option. we can just use the limit
#for i in xrange(search_results.__sizeof__()):
for i in range(0, 24):
score = 0
comments = []
post = search_results.next()
for comment in post.comments:
try:
comment_str = str(comment.body.encode('utf-8'))
comments.append(comment_str)
comments.append(comment.score)
except AttributeError:
pass
for i in range(0, len(comments), 2):
for comment in comments[i].split('\n'):
if any(i in str(comment)
for i in search_string.lower().split(' ')):
#print comment
sentiment = classifier.classify(word_feats(comment))
if sentiment == 'neg':
score += -1 * int(comments[i + 1])
else:
score += 1 * int(comments[i + 1])
if score > 10:
result = str(now) + " POSITIVE " + str(score)
elif score < -10:
result = str(now) + " NEGATIVE " + str(score)
else:
result = str(now) + " NEUTRAL " + str(score)
#return result, max_post_comment, pos_score, max_neg_comment, neg_score
return result
