from TwitterSentimentAnalyzer import TwitterSentimentAnalyzer
from PreProcessor import PreProcessor
er = TwitterSentimentAnalyzer()
processor = PreProcessor()
tmp = "RT @Cj_Walker1: My family aand I have came to a great decision! With that being said I would like to say I have committed to The Ohio State"
clean = processor.preProcess(tmp, lowercase=True)
cleanString = ""
for s in clean:
cleanString = cleanString + s + " "
cleanStringList = [cleanString]
for i in range(39):
cleanStringList.append(" ")
print(cleanString)
print(processor.cleanText((tmp)))
result = er.Evaluate(cleanStringList)
print(result)
'201804030000', '201803300000', '201803290000', '201803280000',
'201803270000'
]
#Get Sentiment Score
er = TwitterSentimentAnalyzer()
processor = PreProcessor()
result = []
for i in range(0, 21):
processedTweets = []
rule = gen_rule_payload("MSFT OR Microsoft",
from_date=day30Start[i],
to_date=day30End[i],
results_per_call=80)
tweets = collect_results(rule,
max_results=80,
result_stream_args=premium_search_args)
for tweet in tweets[0:80]:
r = ' '.join(word for word in processor.preProcess(tweet.all_text))
processedTweets.append(r)
total = 0
for i in range(0, 2):
tmpl = []
for q in range(i * 40, i * 40 + 40):
tmpl.append(processedTweets[q])
s = er.Evaluate(tmpl)
for q in s:
total = total + q
result.append(total)
time.sleep(1)
print(result)
