def run(self):
args = self.get_arguments()
try:
model_file = args[0]
datasets = args[1:]
except IndexError:
raise InvalidParameters
model = SentimentModel()
model = self.train_over_dumps(model, datasets)
model.save(model_file)
return False
def run(self):
args = self.get_arguments()
model_file = args[0]
filenames = args[1:]
if not os.path.exists(model_file):
print("ERROR: model file '%s' doesn't exist" % model_file)
return True
model = SentimentModel.load(model_file)
evaluator = ModelEvaluator(model, filenames)
gold, test = evaluator.get_gold_comparison()
print("Confusion matrix:")
cm = nltk.ConfusionMatrix(gold, test)
print(cm.pp(sort_by_count=True, show_percents=True))
summary = evaluator.evaluate()
precision_sum, recall_sum, fmeasure_sum = 0, 0, 0
print(" TP TN FP FN precision recall f-measure")
rowstr = "%(label)s %(tp)6s %(tn)6s %(fp)6s %(fn)6s %(precision)2.5f %(recall)2.5f %(fmeasure)2.5f"
for sentiment, label in TEXT_LABELS.items():
print(rowstr % {
"label": '%s:' % label.rjust(10),
"tp": summary[sentiment]["true-positives"],
"fp": summary[sentiment]["false-positives"],
"tn": summary[sentiment]["true-negatives"],
"fn": summary[sentiment]["false-negatives"],
"precision": summary[sentiment]["precision"],
"recall": summary[sentiment]["recall"],
"fmeasure": summary[sentiment]["f-measure"],
})
precision_sum += summary[sentiment]["precision"]
recall_sum += summary[sentiment]["recall"]
fmeasure_sum += summary[sentiment]["f-measure"]
precision_avg = precision_sum / len(TEXT_LABELS)
recall_avg = recall_sum / len(TEXT_LABELS)
fmeasure_avg = fmeasure_sum / len(TEXT_LABELS)
print(rowstr % {
"label": "average:".rjust(11),
"tp": "", "fp": "", "tn": "", "fn": "",
"precision": precision_avg,
"recall": recall_avg,
"fmeasure": fmeasure_avg,
})
return False
def run(self):
args = self.get_arguments()
model_file = args[0]
text = ' '.join(args[1:])
if not os.path.exists(model_file):
print("ERROR: model file '%s' doesn't exist" % model_file)
return True
dummytweet = DummyTweet(text)
model = SentimentModel.load(model_file)
label = TEXT_LABELS[model.predict(dummytweet)]
print("Prediction:")
print(" %10s: %s" % (label, text))
return False
def run(self):
args = self.get_arguments()
model_file = args[0]
text = ' '.join(args[1:])
if not os.path.exists(model_file):
print("ERROR: model file '%s' doesn't exist" % model_file)
return True
dummytweet = DummyTweet(text)
model = SentimentModel.load(model_file)
label = TEXT_LABELS[model.predict(dummytweet)]
print("Prediction:")
print(" %10s: %s" % (label, text))
return False
def run(self):
args = self.get_arguments()
model_file = args[0]
filenames = args[1:]
if not os.path.exists(model_file):
print("ERROR: model file '%s' doesn't exist" % model_file)
return True
model = SentimentModel.load(model_file)
reader = DataReader(filenames)
print("Prediction:")
for item in reader.get_tweets():
sentiment, tweet = item
label = TEXT_LABELS[model.predict(tweet)]
print(" %10s: %s" % (label, tweet.text))
return False
def run(self):
args = self.get_arguments()
model_file = args[0]
filenames = args[1:]
if not os.path.exists(model_file):
print("ERROR: model file '%s' doesn't exist" % model_file)
return True
model = SentimentModel.load(model_file)
reader = DataReader(filenames)
print("Prediction:")
for item in reader.get_tweets():
sentiment, tweet = item
label = TEXT_LABELS[model.predict(tweet)]
print(" %10s: %s" % (label, tweet.text))
return False
def run(self):
arguments = self.get_arguments()
try:
model_file = arguments[0]
language = arguments[1]
except (IndexError, ValueError):
raise InvalidParameters
search_terms = arguments[2:]
if not search_terms:
raise InvalidParameters
self.model = SentimentModel.load(model_file)
s = stream.SearchListener(auth.auth(),
search_terms,
self.cb_search,
cb_args=(language, ))
try:
s.run()
except KeyboardInterrupt:
s.close()
return False
def run(self):
arguments = self.get_arguments()
try:
model_file = arguments[0]
language = arguments[1]
except (IndexError, ValueError):
raise InvalidParameters
search_terms = arguments[2:]
if not search_terms:
raise InvalidParameters
self.model = SentimentModel.load(model_file)
s = stream.SearchListener(auth.auth(),
search_terms,
self.cb_search,
cb_args=(language, ))
try:
s.run()
except KeyboardInterrupt:
s.close()
return False