tc=args.tweet_count,
st=args.search_type,
ud=args.since_date,
lan=args.language)
opin_dict = tClient.opinion_mining(search_results['statuses'])
'''
te_search_results = twitter.search(q=keyword, count=tweet_count, result_type='mixed', since_id=since, lang="te")
trans = Translator()
for result in te_search_results['statuses']:
print result['text']
trans.translate(result['text'])
'''
else:
multi_search_results = tClient.multi_search_until(k=args.keyword,
ud=args.since_date,
lan=args.language)
opin_dict = tClient.opinion_mining_multi(multi_search_results)
while True:
print "Print 1)Positive 2)Negative 3)Netral 4)Exit \n "
option = input("Enter option(1/2/3/4)")
opted_dict = {}
if option == 1:
opted_dict = opin_dict['positive']
elif option == 2:
opted_dict = opin_dict['negative']
elif option == 3:
opted_dict = opin_dict['neutral']
elif option == 4:
parser.add_argument("since_date", help = "The date from which tweets should be analyzed")
parser.add_argument("-v", "--verbose", help = "Print traces for debugging", type=bool, default=False)
### End of arguments ###
####main####
args = parser.parse_args()
tClient = TwitterClient()
ReviewRules = [('VeryGood' , 90),
('Good' , 80),
('Watchable', 70),
('Average' , 60),
('Bad' , 30),
('VeryBad' , 0)]
multi_search_results = tClient.multi_search_until(k=args.Movie, ud=args.since_date, verbose=args.verbose)
total_tweets, opinion_dictionary = tClient.review_mining_multi(multi_search_results, ReviewRules)
'''
opinion dictionary is a dictionary of dictionaries has the following structure
{Sentiment : {tweet_id: tweet_text}}
ex: {'VeryGood' : { 1 : "Must Watch",
2 : "Top Notch" } }
'''
result_summary_dictionary = OrderedDict({int: [str,(float, int)]})
'''
{SentimentSNo:[Sentiment,
(approved percetage(percentage of tweets who approved this rating),
