| #dependencies http://code.google.com/p/python-twitter/ | |
| # 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 psycopg2 | |
| def insert_service_alert_sitewide(heading, message, url): | |
| 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("select max(value), extract(dow from max(time)) as dow, \ | |
| extract(year from max(time))::text || lpad(extract(month from max(time))::text,2,'0') \ | |
| || lpad(extract(month from max(time))::text,2,'0') as yearmonthweek, to_char(max(time),'J') \ | |
| from environmentdata_values where \"dataSourceID\"='NSWAEMODemand' \ | |
| group by extract(dow from time), extract(year from time), extract(week from time) \ | |
| order by extract(year from time), extract(week from time), extract(dow from time)") | |
| # 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 get_tweets(user): | |
| tapi = twitter.Api() | |
| return tapi.GetUserTimeline(user) | |
| 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) | |