def evaluate_twitter_methods():
np.random.seed(0)
print "Getting evalution words and embeddings.."
gi = lexicons.load_lexicon("inquirer", remove_neutral=False)
lexicon = lexicons.load_lexicon("twitter", remove_neutral=True)
scores = lexicons.load_lexicon("twitter-scores", remove_neutral=True)
sent140 = lexicons.load_lexicon("140-scores", remove_neutral=False)
# padding lexicon with neutral from GI
gi_neut = [word for word in gi if gi[word] == 0]
gi_neut = np.random.choice(
gi_neut,
int((float(len(gi_neut)) / (len(gi) - len(gi_neut)) * len(lexicon))))
for word in gi_neut:
lexicon[word] = 0
positive_seeds, negative_seeds = seeds.twitter_seeds()
embed = create_representation(
"GIGA", constants.TWITTER_EMBEDDINGS,
set(lexicon.keys()).union(positive_seeds).union(negative_seeds))
print len(
(set(positive_seeds).union(negative_seeds)).intersection(embed.iw))
embed_words = set(embed.iw)
s140_words = set(sent140.keys())
eval_words = [
word for word in lexicon
if word in s140_words and not word in positive_seeds
and not word in negative_seeds and word in embed_words
]
print "Evaluating with ", len(eval_words), "out of", len(lexicon)
print "Sentiment 140"
evaluate(sent140, lexicon, eval_words, tau_lexicon=scores)
print
print "SentProp"
polarities = run_method(positive_seeds,
negative_seeds,
embed,
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.densify,
lr=0.01,
regularization_strength=0.5,
**DEFAULT_ARGUMENTS)
util.write_pickle(polarities, "twitter-test.pkl")
evaluate(polarities, lexicon, eval_words, tau_lexicon=scores)
print "SentProp"
polarities = run_method(
positive_seeds,
negative_seeds,
embed,
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.random_walk,
beta=0.9,
nn=25,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tau_lexicon=scores)
def run_sentprop(subreddit,
ppmi_svd_dir,
socialsent_lexicons_dir,
vocab_dir,
topn=5000,
bstrp=False,
nn=25,
beta=0.9):
#program = 'python make_sent_lexicons.py ' + subreddit + " " + ppmi_svd_dir + " " + socialsent_lexicons_dir + " " + vocab_dir
#os.system(program)
#stop_words = set(stopwords.words('english'))
#stop_words.add('<#S#>') #dummy token
fname = os.path.join(vocab_dir, subreddit + '.txt')
with open(fname, 'r') as f:
words = f.readlines()
top_words = [w.split()[0] for w in words][:topn]
pos_seeds, neg_seeds = seeds.twitter_seeds(
) #Twitter seed words (from socialsent package)
vector_file = os.path.join(ppmi_svd_dir, subreddit + '.txt')
embeddings = create_representation(
'GIGA', vector_file,
set(top_words).union(pos_seeds).union(neg_seeds)) # sub_vecs
if bstrp:
polarities = bootstrap(embeddings,
pos_seeds,
neg_seeds,
return_all=True,
nn=nn,
beta=beta,
num_boots=50,
n_procs=10) # NEW
outfile = os.path.join(socialsent_lexicons_dir,
subreddit + '.pkl') # NEW
util.write_pickle(polarities, outfile) # NEW
else:
polarities = random_walk(embeddings,
pos_seeds,
neg_seeds,
beta=beta,
nn=nn,
num_boots=50,
n_procs=10)
sorted_x = sorted(polarities.items(), key=operator.itemgetter(1))
outfile = os.path.join(socialsent_lexicons_dir, subreddit + '.txt')
with open(outfile, 'w') as f:
tsvin = csv.writer(f, delimiter='\t')
for word in sorted_x:
tsvin.writerow(word)
def evaluate_twitter_methods():
np.random.seed(0)
print "Getting evalution words and embeddings.."
gi = lexicons.load_lexicon("inquirer", remove_neutral=False)
lexicon = lexicons.load_lexicon("twitter", remove_neutral=True)
scores = lexicons.load_lexicon("twitter-scores", remove_neutral=True)
sent140 = lexicons.load_lexicon("140-scores", remove_neutral=False)
# padding lexicon with neutral from GI
gi_neut = [word for word in gi if gi[word] == 0]
gi_neut = np.random.choice(gi_neut, int( (float(len(gi_neut))/(len(gi)-len(gi_neut)) * len(lexicon))))
for word in gi_neut:
lexicon[word] = 0
positive_seeds, negative_seeds = seeds.twitter_seeds()
embed = create_representation("GIGA", constants.TWITTER_EMBEDDINGS, set(lexicon.keys()).union(positive_seeds).union(negative_seeds))
print len((set(positive_seeds).union(negative_seeds)).intersection(embed.iw))
embed_words = set(embed.iw)
s140_words = set(sent140.keys())
eval_words = [word for word in lexicon if word in s140_words and
not word in positive_seeds
and not word in negative_seeds
and word in embed_words]
print "Evaluating with ", len(eval_words), "out of", len(lexicon)
print "Sentiment 140"
evaluate(sent140, lexicon, eval_words, tau_lexicon=scores)
print
print "SentProp"
polarities = run_method(positive_seeds, negative_seeds,
embed,
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.densify,
lr=0.01, regularization_strength=0.5,
**DEFAULT_ARGUMENTS)
util.write_pickle(polarities, "twitter-test.pkl")
evaluate(polarities, lexicon, eval_words, tau_lexicon=scores)
print "SentProp"
polarities = run_method(positive_seeds, negative_seeds,
embed,
method=polarity_induction_methods.bootstrap,
score_method=polarity_induction_methods.random_walk,
beta=0.9, nn=25,
**DEFAULT_ARGUMENTS)
evaluate(polarities, lexicon, eval_words, tau_lexicon=scores)
def main(subreddit):
const = get_constants(subreddit)
word_dict = util.load_pickle(const['DICTS'])
word_dict.filter_extremes(no_above=const['NO_ABOVE_2'],
no_below=const['NO_BELOW'])
to_keep = sorted(word_dict.dfs,
key=lambda w: word_dict.dfs[w],
reverse=True)[:5000]
word_dict.filter_tokens(good_ids=to_keep)
print("Create representation...")
sub_vecs = create_representation('SVD', const['VECS'])
if const["GENDER"]:
pos_seeds, neg_seeds = seeds.gender_seeds()
else:
pos_seeds, neg_seeds = seeds.twitter_seeds()
pos_seeds = list(
set(subredditgen.normalize_text(' '.join(pos_seeds),
const['STEMMING'])))
neg_seeds = list(
set(subredditgen.normalize_text(' '.join(neg_seeds),
const['STEMMING'])))
print("Get sub embedding...")
sub_vecs = sub_vecs.get_subembed(
set(word_dict.token2id.keys()).union(pos_seeds).union(neg_seeds))
print("Bootstrapping...")
print("using seeds {} {}".format(pos_seeds, neg_seeds))
pols = polarity_induction_methods.bootstrap(
sub_vecs,
pos_seeds,
neg_seeds,
return_all=True,
nn=25,
beta=0.9,
boot_size=len(pos_seeds) - 2,
num_boots=30,
n_procs=10,
)
util.write_pickle(pols, const['POLARITIES'])
if __name__ == "__main__":
subreddit = sys.argv[1]
vector_dir = sys.argv[2]
sent_lexicon_dir = sys.argv[3]
vocab_dir = sys.argv[4]
stop_words = set(stopwords.words('english'))
stop_words.add('<#S#>')
fname = os.path.join(vocab_dir, subreddit + '.txt')
with open(fname, 'r') as f:
words = f.readlines()
top_5000 = [w.split()[0] for w in words if w not in stop_words][:5000]
pos_seeds, neg_seeds = seeds.twitter_seeds() #Twitter seed words
vector_file = os.path.join(vector_dir, subreddit + '.txt')
embeddings = create_representation(
'GIGA', vector_file,
set(top_5000).union(pos_seeds).union(neg_seeds))
polarities = bootstrap(embeddings,
pos_seeds,
neg_seeds,
return_all=True,
nn=25,
beta=0.9,
num_boots=2,
n_procs=10)
print polarities[0]