def classify_text():
tweet = request.form['tweet']
start = datetime.datetime.now()
rankings = data.create_ranking(tweet)
end = datetime.datetime.now()
print 'getting city rankings takes: %s' % (end - start)
start = datetime.datetime.now()
top_5_words = feature_selection.top_words_in_tweet(rankings[0][0],tweet)
end = datetime.datetime.now()
print 'getting top 5 words takes: %s' % (end - start)
start = datetime.datetime.now()
cty_corpus_dict = data.city_corpus_dict()
word_count_dict = cty_corpus_dict[rankings[0][0].name]
end = datetime.datetime.now()
print 'getting bogus word count dict takes: %s' % (end - start)
start = datetime.datetime.now()
final_result = []
for word in top_5_words:
final_result.append(word)
names = []
for i in range(0, len(rankings)):
city_name = rankings[i][0].name
names.append(city_name)
end = datetime.datetime.now()
print 'generating lists takes: %s' % (end - start)
return render_template("map.html", tweet=tweet, names=names, rankings=rankings, final_result=final_result)
def classify_text():
tweet = request.form['tweet']
start = datetime.datetime.now()
rankings = data.create_ranking(tweet)
end = datetime.datetime.now()
print 'getting city rankings takes: %s' % (end - start)
start = datetime.datetime.now()
feature_strings_dict = {}
city_corpus_leng_dict = {}
city_tweet_count_dict = {}
for city in cities:
corpus_leng = data.find_leng_city_corpus(city)
city_corpus_leng_dict[city.name] = corpus_leng
city_tweet_count = data.create_region_tweet_count(city)
city_tweet_count_dict[city.name] = city_tweet_count
feature_strings = feature_selection.included_feature_strings(
city, tweet)
feature_strings_dict[city.name] = feature_strings
end = datetime.datetime.now()
print 'getting top 5 words takes: %s' % (end - start)
start = datetime.datetime.now()
cty_corpus_dict = data.city_corpus_dict()
word_count_dict = cty_corpus_dict[rankings[0][0].name]
end = datetime.datetime.now()
print 'getting bogus word count dict takes: %s' % (end - start)
start = datetime.datetime.now()
names = []
for i in range(0, len(rankings)):
city_name = rankings[i][0].name
names.append(city_name)
end = datetime.datetime.now()
print 'generating lists takes: %s' % (end - start)
return render_template("map.html",
tweet=tweet,
city_tweet_count_dict=city_tweet_count_dict,
names=names,
city_corpus_leng_dict=city_corpus_leng_dict,
feature_strings_dict=feature_strings_dict,
rankings=rankings)
def classify_text():
tweet = request.form['tweet']
start = datetime.datetime.now()
rankings = data.create_ranking(tweet)
end = datetime.datetime.now()
print 'getting city rankings takes: %s' % (end - start)
start = datetime.datetime.now()
feature_strings_dict = {}
city_corpus_leng_dict = {}
city_tweet_count_dict = {}
for city in cities:
corpus_leng = data.find_leng_city_corpus(city)
city_corpus_leng_dict[city.name] = corpus_leng
city_tweet_count = data.create_region_tweet_count(city)
city_tweet_count_dict[city.name] = city_tweet_count
feature_strings = feature_selection.included_feature_strings(city, tweet)
feature_strings_dict[city.name] = feature_strings
end = datetime.datetime.now()
print 'getting top 5 words takes: %s' % (end - start)
start = datetime.datetime.now()
cty_corpus_dict = data.city_corpus_dict()
word_count_dict = cty_corpus_dict[rankings[0][0].name]
end = datetime.datetime.now()
print 'getting bogus word count dict takes: %s' % (end - start)
start = datetime.datetime.now()
names = []
for i in range(0, len(rankings)):
city_name = rankings[i][0].name
names.append(city_name)
end = datetime.datetime.now()
print 'generating lists takes: %s' % (end - start)
return render_template("map.html", tweet=tweet, city_tweet_count_dict=city_tweet_count_dict, names=names, city_corpus_leng_dict=city_corpus_leng_dict, feature_strings_dict=feature_strings_dict, rankings=rankings)
