def get_topics_non_dictionary_overlapping(num_news, k, url='http://cnn.com'):
texts = get_news(url, num_news)
gb = SimpleGraphBuilder(text_processing.only_non_dictionary_words, stem_words=False)
gb.load_texts(texts)
G = gb.create_graph()
print "Graph built"
words_by_part = graph_cluster.get_overlap_clusters(G, k, 1)
#print_topics_from_partitions(G, words_by_part, 10)
return G
def get_topics_by_standard_words(num_news, draw=False, url='http://cnn.com'):
texts = get_news(url, num_news)
gb = SimpleGraphBuilder(text_processing.clean_punctuation_and_stopwords)
gb.load_texts(texts)
G = gb.create_graph()
print "Graph built"
partition = community.best_partition(G)
words_by_part = get_words_by_partition(partition)
mod = community.modularity(partition,G)
print("modularity:", mod)
print_topics_from_partitions(G, words_by_part, 10)
if draw:
values = [partition.get(node) for node in G.nodes()]
nx.draw_spring(G, cmap = plt.get_cmap('jet'), node_color = values, node_size=30, with_labels=False)
plt.show()
return G
# for text in texts:
# print text
# print "##################################################"
#
# print "##################################################"
# print "TOKENIZED SENTENCES"
# print "##################################################"
# Show tokenized sentences
for text in gb.text_sentences[:1]:
print "##################################################"
for sentence in text:
print sentence
# Building graph
G = gb.create_graph()
t2 = time.time()
print "Graph built in %.2f sec" %(t2-t1)
# Clustering
# ex = 2
# r = 2
# tol = 1e-3
# threshold = 1e-5
# M = graph_cluster.MCL_cluster(G,ex,r,tol,threshold)
# t3 = time.time()
# print "Graph clustered in %.2f sec" %(t3-t2)
# LOUVAIN
partition = community.best_partition(G)
words_by_part = get_words_by_partition(partition)
# for text in texts:
# print text
# print "##################################################"
#
# print "##################################################"
# print "TOKENIZED SENTENCES"
# print "##################################################"
# Show tokenized sentences
for text in gb.text_sentences[:1]:
print "##################################################"
for sentence in text:
print sentence
# Building graph
G = gb.create_graph()
t2 = time.time()
print "Graph built in %.2f sec" % (t2 - t1)
# Clustering
# ex = 2
# r = 2
# tol = 1e-3
# threshold = 1e-5
# M = graph_cluster.MCL_cluster(G,ex,r,tol,threshold)
# t3 = time.time()
# print "Graph clustered in %.2f sec" %(t3-t2)
# LOUVAIN
partition = community.best_partition(G)
words_by_part = get_words_by_partition(partition)
