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BigData/review_svm.py
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from __future__ import division | |
import os | |
import random | |
import string | |
import sys | |
import nltk | |
from nltk.corpus import movie_reviews | |
import numpy | |
from sklearn.svm import SVC | |
from sklearn.svm import LinearSVC | |
import BagOfWords | |
import XMLParser | |
import TwitterCorpus | |
from TFIDF import delta_tfidf, compute_idfs | |
# Program to classify the movie review dataset using a support vector machine, following Pang and Lee (2002). | |
# "Adapting a technique of Das and Chen (2001), we added the tag NOT to every word between a negation word ('not', | |
# 'isn't', 'didn't', etc.) and the first punctuation mark following the negation word." | |
# They didn't provide a full list. | |
NEGATION_WORDS = ["not", "isn't", "didn't", "doesn't"] | |
PUNCTUATION = [".", "!", "?", ",", ";"] | |
# These are now command line parameters! See below... | |
USE_DELTATFIDF = False # Martineau and Finn. Excludes some other parameters (e.g. frequency) | |
USE_PRESENCE = False # If true, use presence rather than frequency. | |
USE_POS_TAGS = False | |
USE_ADJ_ONLY = False | |
USE_NEGATION = True | |
USE_POSITION = False | |
GRAM_LENGTH = 1 # Unigrams, bigrams, ... TODO use a range | |
NUM_FOLDS = 3 # For cross-validation (Pang & Lee used 3, Martineau & Finin used 10) | |
MIN_OCCURRENCES = 4 # To be included, the word must show up this many times across all documents (Pang and Lee used 4) | |
EPSILON = .001 # determines how long the algorithm runs (default is 0.001) | |
NORMALIZE_BAGS = True | |
USE_LIBLINEAR = True # This is supposedly faster for large instances | |
CORPUS = "movies" # "twitter", "amazon", "movies" | |
USE_DELTA_TFIDF = False | |
def make_folds(documents, ids, num_partitions): | |
folds = [[] for i in range(num_partitions)] | |
fold_ids = [[] for i in range(num_partitions)] | |
for i in range(len(documents)): | |
folds[i % num_partitions].append(documents[i]) | |
fold_ids[i % num_partitions].append(ids[i]) | |
return (folds, fold_ids) | |
def make_bag(text, total_word_counts, **bag_params): | |
return BagOfWords.make(text, ref_bag=total_word_counts, **bag_params) | |
def from_command_line(): | |
i = 0 | |
# Set parameters to default values | |
gram_length = GRAM_LENGTH | |
num_folds = NUM_FOLDS | |
use_presence = USE_PRESENCE | |
use_pos_tags = USE_POS_TAGS | |
use_negation = USE_NEGATION | |
use_position = USE_POSITION | |
min_occurrences = MIN_OCCURRENCES | |
corpus = CORPUS | |
try: | |
args = sys.argv[1:] | |
while i < len(args): | |
if args[i] == "--gram-length": | |
gram_length = int(args[i+1]) | |
i += 2 | |
elif args[i] == "--num-folds": | |
num_folds = int(args[i+1]) | |
i += 2 | |
elif args[i] == "--presence": | |
use_presence = True | |
i += 1 | |
elif args[i] == "--frequency": | |
use_presence = False | |
i += 1 | |
elif args[i] == "--use-pos-tags": | |
use_pos_tags = True | |
i += 1 | |
elif args[i] == "--use-adj-only": | |
use_adj_only = True | |
i += 1 | |
elif args[i] == "--use-negation": | |
use_negation = True | |
i += 1 | |
elif args[i] == "--no-negation": | |
use_negation = False | |
i += 1 | |
elif args[i] == "--use-position": | |
use_position = True | |
i += 1 | |
elif args[i] == "--threshold": | |
min_occurrences = int(args[i+1]) | |
i += 2 | |
elif args[i] == "--corpus": | |
corpus = args[i+1] | |
i += 2 | |
elif args[i] == "--use-delta": | |
use_delta = True | |
i += 1 | |
elif args[i] == "--help": | |
print "Usage:" | |
print "--gram-length N\t\tUse groups of N consecutive words (Default: 1)" | |
print "--num-folds N\t\tUse N folds for cross-validation (Default: 3)" | |
print "--presence\t\tUse word presence rather than word frequency (Default: Off)" | |
print "--frequency\t\tUse word frequency rather than word presence (Default: On)" | |
print "--use-pos-tags\t\tUse part-of-speech tags (Default: Off)" | |
print "--use-negation\t\tTag words appearing after a negation word (Default: Off)" | |
print "--use-adj-only\t\tUse adjectives only (requires --use-pos-tags and --gram-length 1) (Default: Off)" | |
print "--use-position\t\tTag words according to their position in the text (Default: Off)" | |
print "--threshold N\t\tOnly include words that appear at least N times across all documents (Default: 4)" | |
print "\t\t\t(0 < epsilon < 1; lower = more iterations)" | |
print "--corpus\t\tSelect a corpus to evaluate. (amazon, movies, twitter) (Default: movies)" | |
print "--use-delta\t\tUse Delta TFIDF. (Default: Off)" | |
exit() | |
else: | |
print "Error: Invalid argument", args[i] | |
i += 1 | |
classify_reviews(gram_length, num_folds, use_presence, use_negation, use_pos_tags, use_adj_only, min_occurrences, corpus, use_delta) | |
except Exception: | |
print "Invalid arguments" | |
def classify_reviews(gram_length=GRAM_LENGTH, num_folds=NUM_FOLDS, use_presence=USE_PRESENCE, use_negation=USE_NEGATION, use_pos_tags=USE_POS_TAGS, use_adj_only=USE_ADJ_ONLY, | |
use_position = USE_POSITION, min_occurrences=MIN_OCCURRENCES, corpus=CORPUS, use_delta=USE_DELTA_TFIDF, skew=(1,1)): | |
positive_ids = [] | |
negative_ids = [] | |
if corpus == "amazon": | |
# Load the mixed Amazon review dataset. | |
(ids, reviews, labels) = XMLParser.get_all_reviews() | |
for i in range(len(ids)): | |
if labels[i] == 1: | |
positive_ids.append(ids[i]) | |
elif labels[i] == -1: | |
negative_ids.append(ids[i]) | |
elif corpus == "movies": | |
# Load the Pang and Lee sentiment dataset. | |
ids = movie_reviews.fileids() | |
reviews = [list(movie_reviews.words(fileids = [id])) for id in ids] | |
labels = [] | |
for id in ids: | |
label = movie_reviews.categories(id)[0] | |
if label == 'pos': | |
labels.append(1) | |
positive_ids.append(id) | |
elif label == 'neg': | |
labels.append(-1) | |
negative_ids.append(id) | |
elif corpus == "twitter": | |
(ids, reviews, labels) = TwitterCorpus.load() | |
for i in range(len(ids)): | |
if labels[i] == 1: | |
positive_ids.append(ids[i]) | |
elif labels[i] == -1: | |
negative_ids.append(ids[i]) | |
positive_reviews = [] | |
negative_reviews = [] | |
for i in range(len(reviews)): | |
if labels[i] == 1: | |
positive_reviews.append(reviews[i]) | |
elif labels[i] == -1: | |
negative_reviews.append(reviews[i]) | |
num_pos = int(len(positive_reviews) * skew[0]) | |
num_neg = int(len(negative_reviews) * skew[1]) | |
positive_reviews = random.sample(positive_reviews, num_pos) | |
negative_reviews = random.sample(negative_reviews, num_neg) | |
# Partition reviews into folds. | |
(pos_folds, pos_fold_ids) = make_folds(positive_reviews, positive_ids, num_folds) | |
(neg_folds, neg_fold_ids) = make_folds(negative_reviews, negative_ids, num_folds) | |
# Count occurrences of every word across all documents | |
# (this is important for e.g. Delta TFIDF) | |
total_word_counts = {} | |
# Construct a bag of words (or n-grams) from each file. | |
pos_fold_bags = [[] for i in range(num_folds)] | |
neg_fold_bags = [[] for i in range(num_folds)] | |
pos_fold_idfs = [compute_idfs(pos_folds[i]) for i in range(num_folds)] | |
neg_fold_idfs = [compute_idfs(neg_folds[i]) for i in range(num_folds)] | |
bag_params = {'gram_length':gram_length, 'use_presence':use_presence, 'use_negation':use_negation, 'use_pos_tags':use_pos_tags, | |
'use_adj_only':use_adj_only, 'use_position':use_position} | |
for i in range(num_folds): | |
for review in pos_folds[i]: | |
if use_delta: | |
pos_idfs = pos_fold_idfs[i] | |
neg_idfs = neg_fold_idfs[i] | |
pos_fold_bags[i].append(BagOfWords.make_delta_tfidf(review, positive_reviews, negative_reviews, pos_idfs, neg_idfs, total_word_counts)) | |
else: | |
pos_fold_bags[i].append(make_bag(review, total_word_counts, **bag_params)) | |
for review in neg_folds[i]: | |
if use_delta: | |
pos_idfs = pos_fold_idfs[i] | |
neg_idfs = neg_fold_idfs[i] | |
neg_fold_bags[i].append(BagOfWords.make_delta_tfidf(review, positive_reviews, negative_reviews, pos_idfs, neg_idfs, total_word_counts)) | |
else: | |
neg_fold_bags[i].append(make_bag(review, total_word_counts, **bag_params)) | |
# Remove words with less than the minimum occurrences threshold. | |
if min_occurrences > 0: | |
for k in total_word_counts.keys(): | |
if total_word_counts[k] < min_occurrences: | |
for fold in (neg_fold_bags + pos_fold_bags): | |
for bag in fold: | |
if bag.has_key(k): | |
bag.pop(k) | |
total_word_counts.pop(k) | |
avg_acc = 0 | |
wordlist = total_word_counts.keys() | |
for i in range(num_folds): | |
pos_train_reviews = [] | |
neg_train_reviews = [] | |
pos_train_bags = [] | |
neg_train_bags = [] | |
pos_test_reviews = pos_folds[i] | |
neg_test_reviews = neg_folds[i] | |
pos_test_ids = pos_fold_ids[i] | |
neg_test_ids = neg_fold_ids[i] | |
for j in range(num_folds): | |
if j != i: | |
pos_train_reviews += pos_folds[j] | |
neg_train_reviews += neg_folds[j] | |
pos_train_bags += pos_fold_bags[j] | |
neg_train_bags += neg_fold_bags[j] | |
train_labels = [1] * len(pos_train_bags) + [-1] * len(neg_train_bags) | |
train_bags = pos_train_bags + neg_train_bags | |
if USE_LIBLINEAR: | |
classifier = LinearSVC() | |
else: | |
classifier = SVC(kernel="linear",tol=EPSILON) | |
train_vecs = [BagOfWords.to_vector(bag, wordlist) for bag in train_bags] | |
classifier.fit(train_vecs, train_labels) | |
test_bags = pos_fold_bags[i] + neg_fold_bags[i] | |
test_vecs = [BagOfWords.to_vector(bag, wordlist) for bag in test_bags] | |
test_reviews = pos_test_reviews + neg_test_reviews | |
test_ids = pos_test_ids + neg_test_ids | |
test_labels = [1] * len(pos_test_reviews) + [-1] * len(neg_test_reviews) | |
predicted_labels = classifier.predict(test_vecs) | |
acc = classifier.score(test_vecs, test_labels) | |
avg_acc += acc | |
avg_acc /= num_folds | |
return avg_acc | |
def run_configs(): | |
min_occurrences = 4 | |
use_negation = True | |
labels = [] | |
accs = [] | |
#for corpus in ["movies", "amazon", "twitter"]: | |
for corpus in ["amazon", "twitter"]: | |
for use_position in [False, True]: | |
for (use_pos_tags, use_adj_only) in [(False, False), (True, False), (True, True)]: | |
for gram_length in [1,2]: | |
for use_presence in [False, True]: | |
params = {'gram_length':gram_length, 'use_presence':use_presence, 'use_pos_tags':use_pos_tags, 'use_adj_only':use_adj_only, | |
'use_position':use_position, 'corpus':corpus, 'min_occurrences':min_occurrences, 'use_delta':False} | |
acc = classify_reviews(**params) | |
label = "gram_length: %d, use_presence: %s, corpus: %s, use_pos_tags: %s, use_adj_only: %s, use_position: %s" % (gram_length, use_presence, corpus, use_pos_tags, use_adj_only, use_position) | |
print label, acc | |
labels.append(label) | |
accs.append(acc) | |
# Delta-TFIDF construction doesn't support all parameters (yet). | |
params = {'corpus':corpus, 'use_delta':True} | |
acc = classify_reviews(**params) | |
label = "delta_tfidf: True, corpus: %s" % corpus | |
print label, acc | |
labels.append(label) | |
accs.append(acc) | |
return (labels, accs) | |
def run_skewed(): | |
min_occurrences = 4 | |
use_negation = True | |
use_delta = False | |
use_pos_tags = False | |
use_adj_only = False | |
use_position = False | |
use_presence = True | |
labels = [] | |
accs = [] | |
for corpus in ["movies", "amazon"]: | |
for skew in [(0.2,1), (0.4,1), (0.6,1), (0.8, 1), (1,0.8), (1,0.6), (1,0.4), (1,0.2)]: | |
for gram_length in [1,2]: | |
params = {'gram_length':gram_length, 'use_presence':use_presence, 'use_pos_tags':use_pos_tags, 'use_adj_only':use_adj_only, | |
'use_position':use_position, 'corpus':corpus, 'min_occurrences':min_occurrences, 'use_delta':False, 'skew': skew} | |
acc = classify_reviews(**params) | |
label = "corpus: %s, gram_length: %d, skew: (%f, %f)" % (corpus, gram_length, skew[0], skew[1]) | |
print label, acc | |
labels.append(label) | |
accs.append(acc) | |
params = {'gram_length':1, 'use_presence':False, 'use_pos_tags':False, 'use_adj_only':False, | |
'use_position':False, 'corpus':corpus, 'min_occurrences':min_occurrences, 'use_delta':False, 'skew': skew} | |
acc = classify_reviews(**params) | |
label = "corpus: %s, delta_tfidf: True, skew: (%f, %f)" % (corpus, skew[0], skew[1]) | |
print label, acc | |
labels.append(label) | |
accs.append(acc) | |
#(labels, accs) = run_configs() | |
(labels, accs) = run_skewed() | |
f = open('SVM_RESULTS_SKEW.txt', 'w') | |
for (label, acc) in zip(labels, accs): | |
f.write("%s\t%s\n" % (label, acc)) | |
f.close() |