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)
seeds_map=defaultdict(list)
labeled_words=[]
f = open(labeled_words_file)
for l in f:
w, label = l.strip().split('\t')
seeds_map[int(label)].append(w)
labeled_words.append(w)
unlabeled_words=[]
for l in open(unlabeled_words_file):
unlabeled_words.append(l.strip())
embeddings = create_representation("GIGA", embeddings_file, set(unlabeled_words).union(set(labeled_words)))
eval_words = [word for word in embeddings.iw if word not in set(labeled_words)]
# Using SentProp with 10 neighbors and beta=0.99
polarities = random_walk(embeddings, seeds_map, beta=0.7, nn=10, sym=True, arccos=False)
point_estimates = dict([(w,polarities[w].most_common()[0][0]) for w in polarities if w in unlabeled_words])
pickle.dump(polarities, open("{}_{}.pkl".format(output_file_prefix, "socialsent"),'wb'))
df = pd.DataFrame().from_records(point_estimates.items(), columns=['word','label'])
df.to_csv("{}_{}.csv".format(output_file_prefix, "socialsent"), sep='\t', encoding='utf-8')
polarities = label_propagate_probabilistic(embeddings, seeds_map)
point_estimates = dict([(w,polarities[w].most_common()[0][0]) for w in polarities if w in unlabeled_words])
pickle.dump(polarities, open("{}_{}.pkl".format(output_file_prefix, "labelprop"),'wb'))
df = pd.DataFrame().from_records(point_estimates.items(), columns=['word','label'])
df.to_csv("{}_{}.csv".format(output_file_prefix, "labelprop"), sep='\t', encoding='utf-8')
polarities = dist(embeddings, seeds_map)
point_estimates = dict([(w,polarities[w].most_common()[0][0]) for w in polarities if w in unlabeled_words])
pickle.dump(polarities, open("{}_{}.pkl".format(output_file_prefix, "dist"),'wb'))
df.to_csv("{}_{}.csv".format(output_file_prefix, "dist"), sep='\t', encoding='utf-8')
from socialsent import seeds
from socialsent import lexicons
from socialsent.polarity_induction_methods import random_walk
from socialsent.evaluate_methods import binary_metrics
from socialsent.representations.representation_factory import create_representation
if __name__ == "__main__":
print("Evaluting SentProp with 100 dimensional GloVe embeddings")
print("Evaluting only binary classification performance on General Inquirer lexicon")
lexicon = lexicons.load_lexicon("inquirer", remove_neutral=True)
pos_seeds, neg_seeds = seeds.hist_seeds()
embeddings = create_representation("GIGA", "data/example_embeddings/glove.6B.100d.txt",
set(lexicon.keys()).union(pos_seeds).union(neg_seeds))
eval_words = [word for word in embeddings.iw
if not word in pos_seeds
and not word in neg_seeds]
# Using SentProp with 10 neighbors and beta=0.99
polarities = random_walk(embeddings, pos_seeds, neg_seeds, beta=0.99, nn=10,
sym=True, arccos=True)
auc, avg_per = binary_metrics(polarities, lexicon, eval_words)
print("ROC AUC: {:0.2f}".format(auc))
print("Average precision score: {:0.2f}".format(avg_per))
from socialsent import seeds
from socialsent import lexicons
from socialsent.polarity_induction_methods import random_walk
from socialsent.evaluate_methods import binary_metrics
from socialsent.representations.representation_factory import create_representation
if __name__ == "__main__":
print "Evaluting SentProp with 100 dimensional GloVe embeddings"
print "Evaluting only binary classification performance on General Inquirer lexicon"
lexicon = lexicons.load_lexicon("inquirer", remove_neutral=True)
pos_seeds, neg_seeds = seeds.hist_seeds()
embeddings = create_representation("GIGA", "data/example_embeddings/glove.6B.100d.txt",
set(lexicon.keys()).union(pos_seeds).union(neg_seeds))
eval_words = [word for word in embeddings.iw
if not word in pos_seeds
and not word in neg_seeds]
# Using SentProp with 10 neighbors and beta=0.99
polarities = random_walk(embeddings, pos_seeds, neg_seeds, beta=0.99, nn=10,
sym=True, arccos=True)
acc, auc, avg_per = binary_metrics(polarities, lexicon, eval_words)
print "Accuracy with best threshold: {:0.2f}".format(acc)
print "ROC AUC: {:0.2f}".format(auc)
print "Average precision score: {:0.2f}".format(avg_per)