--- a/servicealerts/importer.py
+++ b/servicealerts/importer.py
@@ -1,1 +1,88 @@
+#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)
+