1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | # info # http://stackoverflow.com/questions/4206882/named-entity-recognition-with-preset-list-of-names-for-python-php/4207128#4207128 # http://alias-i.com/lingpipe/demos/tutorial/ne/read-me.html approximate dist # http://streamhacker.com/2008/12/29/how-to-train-a-nltk-chunker/ more training # http://www.postgresql.org/docs/9.1/static/pgtrgm.html # data sources # http://twitter.com/#!/ACTEmergencyInf instant site wide # http://twitter.com/#!/ACTPol_Traffic # http://esa.act.gov.au/feeds/currentincidents.xml # source: https://gist.github.com/322906/90dea659c04570757cccf0ce1e6d26c9d06f9283 import nltk import twitter import tweepy import psycopg2 from iniparse import INIConfig def insert_service_alert_sitewide(heading, message, url): print "NaN" def insert_service_alert_for_street(streets, heading, message, url): conn_string = "host='localhost' dbname='energymapper' user='postgres' password='snmc'" # print the connection string we will use to connect print "Connecting to database\n ->%s" % (conn_string) try: # get a connection, if a connect cannot be made an exception will be raised here conn = psycopg2.connect(conn_string) # conn.cursor will return a cursor object, you can use this cursor to perform queries cursor = conn.cursor() # execute our Query cursor.execute("") # retrieve the records from the database records = cursor.fetchall() for record in records: ys.append(record[0]) # >>> cur.execute("INSERT INTO test (num, data) VALUES (%s, %s)", (42, 'bar')) #>>> cur.statusmessage #'INSERT 0 1' except: # Get the most recent exception exceptionType, exceptionValue, exceptionTraceback = sys.exc_info() # Exit the script and print an error telling what happened. sys.exit("Database connection failed!\n ->%s" % (exceptionValue)) def extract_entity_names(t): entity_names = [] if hasattr(t, 'node') and t.node: if t.node == 'NE': entity_names.append(' '.join([child[0] for child in t])) else: for child in t: entity_names.extend(extract_entity_names(child)) return entity_names def extract_names(sample): sentences = nltk.sent_tokenize(sample) tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences] tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences] chunked_sentences = nltk.batch_ne_chunk(tagged_sentences, binary=True) # chunked/tagged may be enough to just find and match the nouns entity_names = [] for tree in chunked_sentences: # Print results per sentence # print extract_entity_names(tree) entity_names.extend(extract_entity_names(tree)) # Print all entity names #print entity_names # Print unique entity names print set(entity_names) cfg = INIConfig(open('/tmp/aws.ini')) auth = tweepy.OAuthHandler(cfg.api_keys.twitter_consumer_key, cfg.api_keys.twitter_consumer_secret) auth.set_access_token(cfg.api_keys.twitter_access_token, cfg.api_keys.twitter_access_token_secret) api = tweepy.API(auth) # If the authentication was successful, you should # see the name of the account print out print api.me().name # https://github.com/tweepy/tweepy/blob/master/tweepy/api.py print api.user_timeline(screen_name="ACTPol_Traffic") print api.update_status(status="test") |