def sentimentAnalysis(self, tweets, month="0"):
if month == "0":
enum = {"Jan":31, "Feb":28, "Mar":31, "Apr":30 , "May":31, "Jun":30, "Jul":31, "Aug":31, "Sep":30, "Oct":31, "Nov":30, "Dec":31}
tweetMonth = {"Jan":[],"Feb":[],"Mar":[],"Apr":[],"May":[],"Jun":[],"Jul":[],"Aug":[],"Sep":[],"Oct":[],"Nov":[], "Dec":[]}
pos = neg = neu = 0
s = Sentianal()
for tw in tweets:
for t in tweets[tw]:
count = 0
for i in range(0, enum[tw]):
date_temp = t['created_at'].split(" ")
date = int(date_temp[2])
if date == (i+1):
sentimentScore = s.compute(t['text_clean']) #compute sentiment score of a tweet
if sentimentScore > 0.0: #if positive
tweets[tw][count]['sentiment'] = 'positive'
pos = pos + 1 #increment the number of positive tweets
elif sentimentScore < 0.0: #if negative
tweets[tw][count]['sentiment'] = 'negative'
neg = neg + 1 #increment the number of negative tweets
else:
tweets[tw][count]['sentiment'] = 'neutral'
neu = neu + 1
count = count + 1
return (tweets, pos, neg, neu)
else:
enum = {"Jan":31, "Feb":28, "Mar":31, "Apr":30 , "May":31, "Jun":30, "Jul":31, "Aug":31, "Sep":30, "Oct":31, "Nov":30, "Dec":31}
tweetMonth = {"Jan":[],"Feb":[],"Mar":[],"Apr":[],"May":[],"Jun":[],"Jul":[],"Aug":[],"Sep":[],"Oct":[],"Nov":[], "Dec":[]}
pos = neg = neu = 0
s = Sentianal()
for t in tweets[month]:
count = 0
for i in range(0, enum[month]):
date_temp = t['created_at'].split(" ")
date = int(date_temp[2])
if date == (i+1):
sentimentScore = s.compute(t['text_clean']) #compute sentiment score of a tweet
if sentimentScore > 0.0: #if positive
tweets[month][count]['sentiment'] = 'positive'
pos = pos + 1 #increment the number of positive tweets
elif sentimentScore < 0.0: #if negative
tweets[month][count]['sentiment'] = 'negative'
neg = neg + 1 #increment the number of negative tweets
else:
tweets[month][count]['sentiment'] = 'neutral'
neu = neu + 1
count = count + 1
return (tweets, pos, neg, neu)
from sentianal import Sentianal
import numpy as numpy
from pandas import *
import matplotlib.pyplot as plt
##################### you can modify this part ######################
corpusFile = "jokowitweet.txt"
positiveFile = "jokowipositivetweet.txt"
negativeFile = "jokowinegativetweet.txt"
#####################################################################
s = Sentianal()
fin = open(corpusFile, "r")
foutp = open(positiveFile, "w")
foutn = open(negativeFile, "w")
pos = 0
neg = 0
for line in fin:
line = line.strip()
sentimentScore = s.compute(line) #compute sentiment score of a tweet
if sentimentScore > 0.0: #if positive
pos += 1 #increment the number of positive tweets
foutp.write(line) #write the positive tweet in a new file (separated)
foutp.write("\n")
prab_data = DataFrame(tweet)
#rename the column
prab_data.columns = ['tweet']
#score the tweet
score = []
#create normalizer object
norm = Normalizer()
#create Stop word Removal object
st = StpRemoval()
#create sentiment analysis object
s = Sentianal()
for i in range(0, len(prab_data)):
#normalize
line = norm.normalize(prab_data['tweet'][i])
#remove stopword
line = st.removeStp(line)
#score sentiment
score.append(s.compute(line))
#join the dataframe
score_data = DataFrame(score)
prab_data = prab_data.join(score_data)
prab_data.columns = ['tweet_processed', 'score']
prab_data = DataFrame(tweet) #rename the column prab_data.columns = ['tweet'] #score the tweet score = [] #create normalizer object norm = Normalizer() #create Stop word Removal object st = StpRemoval() #create sentiment analysis object s = Sentianal() for i in range(0,len(prab_data)): #normalize line = norm.normalize(prab_data['tweet'][i]) #remove stopword line = st.removeStp(line) #score sentiment score.append(s.compute(line)) #join the dataframe score_data = DataFrame(score) prab_data = prab_data.join(score_data) prab_data.columns = ['tweet_processed','score']
from sentianal import Sentianal
import numpy as numpy
from pandas import *
import matplotlib.pyplot as plt
##################### you can modify this part ######################
corpusFile = "jokowitweet.txt"
positiveFile = "jokowipositivetweet.txt"
negativeFile = "jokowinegativetweet.txt"
#####################################################################
s = Sentianal()
fin = open(corpusFile, "r")
foutp = open(positiveFile, "w")
foutn = open(negativeFile, "w")
pos = 0
neg = 0
for line in fin:
line = line.strip()
sentimentScore = s.compute(line) #compute sentiment score of a tweet
if sentimentScore > 0.0: #if positive
pos += 1 #increment the number of positive tweets
foutp.write(line) #write the positive tweet in a new file (separated)
foutp.write("\n")
