if args.train:
print("Forcing the model to retrain")
model = train(args.eval)
else:
try:
model = joblib.load(model_filename)
except:
model = train(args.eval)
cv = model['vec']
clf = model['clf']
if args.tweets_file is not None:
with open(args.tweets_file, 'r') as f:
tweets = f.read().split('\n')
df = pd.DataFrame(tweets, columns=['text'])
else:
client = TwitterClient()
tweets = client.get_tweets(query=args.query, count=200)
df = pd.DataFrame(tweets)
tc = TextCleaner()
cleaned_text = tc.fit_transform(df.text)
counts = cv.transform(cleaned_text)
preds = clf.predict(counts)
for text, pred in zip(df.text, preds):
print("\nSentiment: %s. Tweet: %s" % (pred, text))
