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self.start_time) < self.limit: 44 | full_data = json.loads(tweet) 45 | created_time = full_data['created_at'] 46 | user_id = full_data['id_str'] 47 | text = full_data['text'] 48 | document_record = {'time':created_time, 'user_id': user_id, 'text':text} 49 | print text 50 | # insert the document into mongoDB 51 | self.db.tweets.insert_one(document_record) 52 | return True 53 | else: 54 | return False 55 | 56 | def on_error(self, status_code): 57 | print status_code 58 | return True 59 | 60 | def on_timeout(self): 61 | return True 62 | 63 | 64 | # ******** Main Program ********************* 65 | 66 | # parameters for Twitter App (please use your parameters here) 67 | consumer_key = '******************' 68 | consumer_secret = '******************' 69 | access_key = '******************' 70 | access_secret = '******************' 71 | 72 | # authentication 73 | auth = tweepy.OAuthHandler(consumer_key, consumer_secret) 74 | auth.set_access_token(access_key, access_secret) 75 | api = tweepy.API(auth) 76 | sapi = tweepy.streaming.Stream(auth, CustomStreamListener(api)) 77 | # the list of keywords for filtering tweets 78 | keyword_list = ['Election'] 79 | sapi.filter(track = keyword_list, languages = ['en']) 80 | print 'Tweets have been successfully stored into mongoDB.' 81 | 82 | # *** retrive data from mongoDB *** 83 | conn =pymongo.MongoClient('localhost', 27017) 84 | print 'Connected successfully to MongoDB!' 85 | # create a database 86 | db_name='db2' 87 | db=conn[db_name] 88 | # collection 89 | colection = db.tweets 90 | # query: find all documents 91 | results = colection.find() 92 | # close the mongoDB connection 93 | conn.close() 94 | # convert the results to a list 95 | list_results=list(results) 96 | # print the time and the text 97 | for record in list_results: 98 | print 'At %s: \t %s.'% (record['time'],record['text']) 99 | 100 | # *** word frequency mining **** 101 | # tokenizer 102 | tweet_tokenizer = TweetTokenizer() 103 | # punctuation list 104 | punct = list(string.punctuation) 105 | # download 127 Englisg stop words 106 | import nltk 107 | nltk.download('stopwords') 108 | # list of stop words and punctuations 109 | stopword_list = stopwords.words('english') + punct + ['rt', 'via'] 110 | 111 | # record the number of occurences for each word 112 | tf = Counter() 113 | all_dates = [] 114 | 115 | # get the text and the time 116 | for element in list_results: 117 | message = element['text'] 118 | tokens = process(text = message, tokenizer = tweet_tokenizer, stopwords = stopword_list) 119 | all_dates.append(element['time']) 120 | # update word frequency 121 | tf.update(tokens) 122 | 123 | # convert the counter to a sorted list (tf_sorted is a list of 2-tuples) 124 | tf_list_sorted = sorted(tf.items(), key = lambda pair: pair[1], reverse = True) 125 | # print each word and its frequency 126 | for item in tf_list_sorted: 127 | print item[0], item[1] 128 | 129 | # print the top-30 frequent words and their frequencies 130 | y1 = [x[1] for x in tf_list_sorted[:30]] 131 | x1 = range(1, len(y1) + 1) 132 | fig1 = plt.figure() 133 | plt.bar(x1, y1) 134 | plt.xlabel("Word index") 135 | plt.title("Term Frequencies") 136 | plt.ylabel("Frequency") 137 | fig1.savefig('term_distribution.jpg') 138 | 139 | # *** tweet time series **** 140 | ones = np.ones(len(all_dates)) 141 | idx = pd.DatetimeIndex(all_dates) 142 | # the actual time series 143 | original_series = pd.Series(ones, index = idx).sort_index() 144 | # time series with step of 10 seconds 145 | revised_series = original_series.resample('10S').sum() 146 | 147 | # print the time series 148 | x2 = [x*10 for x in range(len(revised_series))] 149 | y2 = list(revised_series) 150 | fig2 = plt.figure() 151 | plt.bar(x2, y2) 152 | plt.title("Time series for real-time tweets") 153 | plt.ylabel("Number of tweets") 154 | plt.xlabel("Time [S]") 155 | fig2.savefig('tweet_time_series.jpg') 156 | 157 | --------------------------------------------------------------------------------