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 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))