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BigData/LexiconEval.py
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from __future__ import division | |
import sys | |
import nltk | |
from nltk.corpus import movie_reviews | |
import MPQALexicon | |
import AniaLexicon | |
import GlossLexicon | |
USE_STEMMING = False | |
USE_PARSING = True | |
LEX_ALG = "gloss" | |
LEX_SOURCE = "mpqa" | |
# new and improved finite state machine | |
# states are as follows: | |
# 0 - base | |
# 1 - negator found | |
# 2 - intensifier found | |
# 3 - un-intensifier found (unused) | |
# 4 - negator + intensifier found | |
def calculate_score(text, lexicon): | |
negators = ["not", "n't", "hardly", "barely"] | |
intensifiers = ["very", "really", "incredibly", "amazingly", "extremely"] | |
if USE_STEMMING: | |
negators = do_stem(negators) | |
intensifiers = do_stem(intensifiers) | |
punctuation = [".", "!", "?", ",", ";", '(', ')'] | |
state = 0 | |
score = 0 | |
num_double = 0 | |
num_single = 0 | |
num_neg = 0 | |
num_halfneg = 0 | |
for word in text: | |
if state == 0: | |
if lexicon.has_key(word): | |
score += lexicon[word] | |
num_single += 1 | |
elif word in negators: | |
state = 1 | |
elif word in intensifiers: | |
state = 2 | |
elif state == 1: | |
if lexicon.has_key(word): | |
score += -1 * lexicon[word] | |
num_neg += 1 | |
state = 0 | |
elif word in intensifiers: | |
state = 4 | |
else: | |
state = 0 | |
elif state == 2: | |
if lexicon.has_key(word): | |
score += 2 * lexicon[word] | |
num_double += 1 | |
state = 0 | |
else: | |
state = 0 | |
elif state == 3: | |
pass #TODO | |
elif state == 4: | |
if lexicon.has_key(word): | |
score += -0.5 * lexicon[word] | |
num_halfneg += 1 | |
state = 0 | |
else: | |
state = 0 | |
#print num_single, num_neg, num_double, num_halfneg | |
return score | |
def do_stem(text): | |
global stemmer | |
return [stemmer.stem(word) for word in text] | |
def get_label(id): | |
return movie_reviews.categories(fileids=[id])[0] | |
i = 0 | |
try: | |
args = sys.argv[1:] | |
while i < len(args): | |
if args[i] in ["--alg", "--algorithm"]: | |
if args[i+1] == "gloss": | |
LEX_ALG = "gloss" | |
elif args[i+1] == "conjunction": | |
LEX_ALG = "conjunction" | |
else: | |
print "Invalid algorithm" | |
i += 2 | |
elif args[i] in ["--lex", "--lexicon"]: | |
if args[i+1] == "mpqa": | |
LEX_SOURCE = "mpqa" | |
elif args[i+1] == "ania": | |
LEX_SOURCE = "ania" | |
else: | |
print "Invalid lexicon" | |
i += 2 | |
elif args[i] == "--help": | |
print "Usage:" | |
print "--alg X: Choose the algorithm to use ('gloss', 'conjunction' or 'none') (default: gloss)" | |
print " - gloss: Use the gloss-based algorithm (Esuli & Sebastiani)" | |
print " - conjunction: Use the conjunction-based algorithm (Hatzivassiloglou & McKeown)" | |
print "--lexicon X: Choose the lexicon to use ('mpqa', 'ania' or 'none')" | |
print " - mpqa: Use the MPQA lexicon" | |
print " - ania: Use the hand-labeled lexicon from the Brown corpus" | |
exit() | |
else: | |
print "Error: Invalid argument", args[i] | |
i += 1 | |
except Exception: | |
print "Invalid arguments" | |
exit() | |
print "Lexicon =", LEX_SOURCE | |
print "Algorithm =", LEX_ALG | |
# Load the test set. A few options here. | |
if LEX_SOURCE == "mpqa": | |
(test_words, test_labels) = MPQALexicon.load(True) | |
elif LEX_SOURCE == "ania": | |
(test_words, test_labels) = AniaLexicon.load() | |
else: | |
print "Invalid lexicon" | |
exit() | |
if USE_STEMMING: | |
stemmer = nltk.stem.porter.PorterStemmer() | |
test_words = do_stem(test_words) | |
if LEX_ALG == "gloss": | |
lexicon = GlossLexicon.create(test_words, test_labels) | |
elif LEX_ALG == "conjunction": | |
print "Error: Conjunction algorithm NYI" | |
elif LEX_ALG == "none": | |
lexicon = create_lexicon(test_words, test_labels) | |
correct = len([(word, label) for (word, label) in zip(test_words, test_labels) if lexicon.has_key(word) and label == lexicon[word]]) | |
lex_acc = correct/len(lexicon.items()) | |
print "Lexicon accuracy:", lex_acc | |
# Iterate through all of the reviews and compute scores by taking the sum of their | |
# component lexicon words. Includes rudimentary negation testing. | |
correct = 0 | |
positive = 0 | |
ids = sorted(movie_reviews.fileids()) | |
scores = [] | |
for id in ids: | |
words = list(movie_reviews.words(fileids=[id])) | |
if USE_STEMMING: | |
words = do_stem(words) | |
if USE_PARSING: | |
score = calculate_score(words, lexicon) | |
else: | |
score = 0 | |
for word in words: | |
if lexicon.has_key(word): | |
score += lexicon[word] | |
x += 1 | |
scores.append(score) | |
#print id, score | |
for i in range(len(ids)): | |
id = ids[i] | |
score = scores[i] | |
if score >= 0: | |
sent_value = "pos" | |
positive += 1 | |
#print id, sent_value | |
elif score < 0: | |
sent_value = "neg" | |
#print id, sent_value | |
label = get_label(id) | |
if sent_value == label: | |
correct += 1 | |
print "correct:", correct/len(ids) | |
print "positive:", positive/len(ids) |