confessions = db.parses.find({
"analyzed": { "$ne": ANALYZER_VERSION }
}, limit=500)
threshold = 0.2
for confession in confessions:
for tree_id, raw_tree in enumerate(confession["trees"]):
if raw_tree == "None": continue
# get sentence categories
tree = Tree.fromstring(raw_tree)
categories = [(category, prob) for (category, prob) in classifier.classify(tree).items() if prob > threshold]
# add to queue to get sentence sentiment
predictor.add_tree({
"raw_tree": raw_tree,
"tree_id": tree_id,
"categories": categories,
"confession_id": confession["_id"]
})
# run sentiment predictor
predictor.run()
# aggregate by confession
confession_results = {}
for datum in predictor.trees:
if datum["confession_id"] not in confession_results: confession_results[datum["confession_id"]] = []
confession_results[datum["confession_id"]].append(datum)
for confession_id, sentences in confession_results.items():
categories = {}
probabilities = {}
s = datum["sentiment"]
if datum["gold_sentiment"] < 0:
if s < 0: accurate += 1
else: inaccurate += 1
else:
if s > 0: accurate += 1
else: inaccurate += 1
return accurate*1.0/(accurate+inaccurate)
# predict sentence sentiments
predictor = SentimentPredictor()
for (parse_id, tree_id), sentiments in sentences.items():
confession = db.parses.find_one({ "_id": ObjectId(parse_id) })
tree = confession["trees"][tree_id]
sentiment = sum(sentiments)*1.0/len(sentiments)
predictor.add_tree({
"raw_tree": tree,
"gold_sentiment": sentiment
})
predictor.run()
print score_accuracy(predictor.trees)
# predict confession sentiments
predictor = SentimentPredictor()
for parse_id, sentiments in confessions.items():
confession = db.parses.find_one({ "_id": ObjectId(parse_id) })
confession["gold_sentiment"] = sum(sentiments)*1.0/len(sentiments)
predictor.add_confession(confession)
predictor.run()
print score_accuracy(predictor.confessions)