def get_opinion(lang, lat, long, rad, name):
search.work(str(lang), str(lat) + ',' + str(long) + ',' + str(int(rad)) + 'mi')
news.getNews(name)
opinion = sentiment.run_sentiment_analysis('tweets.txt')
opinion += sentiment.run_sentiment_analysis('news.txt')
# Check for + or - in front of opinion
if opinion > 0:
opinion = "+" + str(opinion)
elif opinion < 0:
opinion = str(opinion)
else:
opinion = str(opinion)
# Make dictionary for send
senti = [
{
'Language': lang,
'Latitude': lat,
'Longitude': long,
'Radius': rad,
'Opinion': opinion
}
]
logging.info("Opinion of city is: " + opinion)
return jsonify(senti)
def write_processed_tweets(raw_tweet_file, keyword_file, output_file):
partitioned_tweets = partition.partition(raw_tweet_file, keyword_file)
sentiment_tweets = {}
for candidate, tweets in partitioned_tweets.items():
sentiment_tweets[candidate] = sentiment.run_sentiment_analysis(
tweets, 'words')
with open(output_file, 'w') as data_file:
data_file.write('{\n')
first_candidate = True
for candidate, tweets in sentiment_tweets.items():
if first_candidate:
first_candidate = False
else:
data_file.write(',\n')
data_file.write('"' + candidate + '": [\n')
first_item = True
for tweet in tweets:
if first_item:
first_item = False
else:
data_file.write(',\n')
data_file.write(json.dumps(tweet))
data_file.write(']\n')
data_file.write('}')
def get_opinion(lang, lat, long, rad, name):
search.work(str(lang), str(lat) + ',' + str(long) + ',' + str(int(rad)) + 'mi')
news.getNews(name)
twitterOpinion = sentiment.run_sentiment_analysis('tweets.txt')
newsOpinion = sentiment.run_sentiment_analysis('news.txt')
visual = images.process_image_search(name)
# Check for + or - in front of opinion
if newsOpinion > 0:
newsOpinion = "+" + str(newsOpinion)
elif newsOpinion < 0:
newsOpinion = str(newsOpinion)
else:
newsOpinion = str(newsOpinion)
if twitterOpinion > 0:
twitterOpinion = "+" + str(twitterOpinion)
elif twitterOpinion < 0:
twitterOpinion = str(twitterOpinion)
else:
twitterOpinion = str(twitterOpinion)
# Make dictionary for send
senti = [
{
'Language': lang,
'Latitude': lat,
'Longitude': long,
'Radius': rad,
'News Opinion': news,
'Twitter Opinion': twitterOpinion,
'Opinion': twitterOpinion + newsOpinion,
'Visual' : visual
}
]
logging.info("Opinion of city is: " + twitterOpinion + newsOpinion)
return jsonify(senti)
def write_processed_tweets(raw_tweet_file, keyword_file, output_file):
partitioned_tweets = partition.partition(raw_tweet_file, keyword_file)
sentiment_tweets = {}
for candidate, tweets in partitioned_tweets.items():
sentiment_tweets[candidate] = sentiment.run_sentiment_analysis(tweets, 'words')
with open(output_file, 'w') as data_file:
data_file.write('{\n')
first_candidate = True
for candidate, tweets in sentiment_tweets.items():
if first_candidate:
first_candidate = False
else:
data_file.write(',\n')
data_file.write('"' + candidate + '": [\n')
first_item = True
for tweet in tweets:
if first_item:
first_item = False
else:
data_file.write(',\n')
data_file.write(json.dumps(tweet))
data_file.write(']\n')
data_file.write('}')
def run_analysis(filename, keyword_file, print_all = True):
partitioned_tweets = partition.partition(filename, keyword_file)
analyzed_tweets = {}
for candidate, tweets in partitioned_tweets.items():
analyzed_tweets[candidate] = sentiment.run_sentiment_analysis(tweets, 'words')
predict.predict(analyzed_tweets, print_all)