class Classifier:
def __init__(self, tvshow):
self.tvshow = tvshow
self.nb = NBModel()
self.client = ImdbClient()
def classifyAll(self):
possible_shows = ['Walking Dead', \
'Arrow', \
'Family Guy', \
'Big bang Theory', \
'South Park', \
'American Horror Story', \
'Modern Family', \
'Heroes Reborn']
reviews = []
for show in possible_shows:
reviews.append(self.client.searchShow(show))
self.nb.naive_bayes_train(reviews)
self.nb.nb_classify_tweets(self.tvshow, self.client.readFromMongo(parse_show(self.tvshow), sys.maxint))
def nbClassify(self):
reviews = self.client.searchShow(self.tvshow)
self.nb.naive_bayes_train(reviews)
self.nb.nb_classify_tweets(self.tvshow, self.client.readFromMongo(parse_show(self.tvshow), sys.maxint))
class Statistics:
def __init__(self):
self.imdb = ImdbClient()
# returns tuple: (sum, num_items, predicted rating, current IMDB rating, percent error)
def get_stats(self, tvshow, sum, num_items):
current_imdb_rating = self.imdb.getCurrentImdbRating(tvshow)
predicted_value = float(sum) / num_items
return sum, num_items, predicted_value, current_imdb_rating, \
float(current_imdb_rating - predicted_value) / float(current_imdb_rating)
def printStats(self, tvshow, sum, numItems):
currentImdbRating = self.imdb.getCurrentImdbRating(tvshow)
predictedValue = float(sum) / numItems
print("---------- Statistics -----------")
print("Sum of the ratings from Twitter: ", sum)
print("Number of classified ratings: ", numItems)
print("Average value: ", predictedValue)
print("Current IMDB rating: ", currentImdbRating)
print("Current error: ", float(currentImdbRating - predictedValue) / float(currentImdbRating))
class Classifier:
def __init__(self, tvshow):
self.tvshow = tvshow
self.nb = NBModel()
self.client = ImdbClient()
def classifyAll(self):
possible_shows = ['Walking Dead', \
'Arrow', \
'Family Guy', \
'Big bang Theory', \
'South Park', \
'American Horror Story', \
'Modern Family', \
'Heroes Reborn']
reviews = []
for show in possible_shows:
reviews.append(self.client.searchShow(show))
self.nb.nb_train_text(reviews)
self.nb.nb_classify_tweets(self.tvshow, self.client.readFromMongo(parse_show(self.tvshow), sys.maxint))
def nb_train(self):
reviews = self.client.searchShow(self.tvshow)
self.nb.nb_train_text(reviews)
self.nb.save_model()
def nb_train_all_episodes(self):
# General show specific reviews
reviews = self.client.searchShow(self.tvshow)
episodeNames = self.client.get_all_episode_names(self.tvshow)
for name in episodeNames:
episodeShow = name + " " + self.tvshow #(self.tvshow).join(unicodedata.normalize('NFKD', name).encode('ascii', 'ignore'))
query = self.client.searchShow(episodeShow)
episodeShow = ''
if query is not None:
reviews.append(query)
self.nb.nb_train_text(reviews)
def nbClassify(self):
return self.nb.nb_classify_tweets(self.tvshow,
self.client.readFromMongo(parse_show(self.tvshow), sys.maxint))
def __init__(self, tvshow):
self.tvshow = tvshow
self.nb = NBModel()
self.client = ImdbClient()
def __init__(self):
self.imdb = ImdbClient()
