def load(path):
obj = Guesser()
obj._cl = Classifier.load(path)
return obj
q = sys.argv[1]
cached = r.get(q)
if not cached:
url = 'http://search.twitter.com/search.json'
req = requests.get(url, params={'q': sys.argv[1]})
data = json.loads(req.text)
if 'results' not in data:
print('Error')
print(data)
exit()
cached = json.dumps(data['results'])
r.setex(q, TTL, cached)
results = json.loads(cached)
cl = Classifier.load('test.svm')
index = ir.SentimentIndex.load('test.index', 'delta', 'bogram')
index.get_text = lambda x: x['text']
docs = []
for msg in results:
feats = index.weight(index.features(msg))
docs.append(feats)
labels = cl.predict(docs)
for n in range(len(results)):
print('{0}.\t{1}\t{2}'.format(n + 1, labels[n], results[n]['text']))
def load(path): obj = Guesser() obj._cl = Classifier.load(path) return obj
