def main():
data.city_corpus_dict()
#seeds words table with city, word, prob(w/city)
data.seed_words_table()
data.city_tweet_corpus_dict()
for city in cities:
data.create_region_tweet_count(city)
feature_selection.populate_db_with_features(city)
data.create_tweet_total_count()
def main():
data.city_corpus_dict()
#seeds words table with city, word, prob(w/city)
data.seed_words_table()
data.city_tweet_corpus_dict()
for city in cities:
data.create_region_tweet_count(city)
feature_selection.populate_db_with_features(city)
data.create_tweet_total_count()
def list_features(city_name):
cities = data.cities
city = None
for i in range(0, len(cities)):
if cities[i].name == city_name:
feature_list = feature_selection.get_hardcoded_features(cities[i])
city = cities[i]
if city:
latitude = city.lat
longitude = city.lon
city_name = city.name
city_tweet_count = data.create_region_tweet_count(city)
city_word_count = data.find_leng_city_corpus(city)
return render_template("city_words.html", features= feature_list, city_name=city_name, latitude=latitude, longitude=longitude, city_tweet_count=city_tweet_count, city_word_count=city_word_count)
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 get_tweet_word_counts(word, city):
city_tweet_corpus_dict = data.city_tweet_corpus_dict()
word_tweet_count_dic = city_tweet_corpus_dict[city.name]
N11 = word_tweet_count_dic.get(word,0)
Ndot_1 = data.create_region_tweet_count(city)
N01 = Ndot_1 - N11
N = data.create_tweet_total_count()
Ndot_0 = N - Ndot_1
N1_dot = 0
for c in cities:
tweet_corpus_d = city_tweet_corpus_dict[c.name]
N1_dot += tweet_corpus_d.get(word, 0)
N10 = N1_dot - N11
N00 = Ndot_0 - N10
N0_dot = N00 + N01
return {
'N10': float(N10), 'N11': float(N11), 'N01': float(N01), 'N00': float(N00)}
def list_features(city_name):
cities = data.cities
city = None
for i in range(0, len(cities)):
if cities[i].name == city_name:
feature_list = feature_selection.get_hardcoded_features(cities[i])
city = cities[i]
if city:
latitude = city.lat
longitude = city.lon
city_name = city.name
city_tweet_count = data.create_region_tweet_count(city)
city_word_count = data.find_leng_city_corpus(city)
return render_template("city_words.html",
features=feature_list,
city_name=city_name,
latitude=latitude,
longitude=longitude,
city_tweet_count=city_tweet_count,
city_word_count=city_word_count)
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 get_tweet_word_counts(word, city):
city_tweet_corpus_dict = data.city_tweet_corpus_dict()
word_tweet_count_dic = city_tweet_corpus_dict[city.name]
N11 = word_tweet_count_dic.get(word, 0)
Ndot_1 = data.create_region_tweet_count(city)
N01 = Ndot_1 - N11
N = data.create_tweet_total_count()
Ndot_0 = N - Ndot_1
N1_dot = 0
for c in cities:
tweet_corpus_d = city_tweet_corpus_dict[c.name]
N1_dot += tweet_corpus_d.get(word, 0)
N10 = N1_dot - N11
N00 = Ndot_0 - N10
N0_dot = N00 + N01
return {
'N10': float(N10),
'N11': float(N11),
'N01': float(N01),
'N00': float(N00)
}
