def worker_function(file_name_list,
source_folder,
target_folder):
source_path_list = (source_folder + "/" + file_name for file_name in file_name_list)
target_path_list = (target_folder + "/" + file_name[:-4] + ".json" for file_name in file_name_list)
sent_tokenize, _treebank_word_tokenize = get_tokenizer()
tagger = get_braupt_tagger()
lemmatizer, lemmatize = get_lemmatizer("wordnet")
stopset = get_stopset()
first_cap_re, all_cap_re = get_camel_case_regexes()
digits_punctuation_whitespace_re = get_digits_punctuation_whitespace_regex()
pos_set = get_pos_set()
# Get the lists of a user
for source_path in source_path_list:
twitter_lists_corpus = load_pickle(source_path)
if "lists" in twitter_lists_corpus.keys():
twitter_lists_corpus = twitter_lists_corpus["lists"]
else:
continue
bag_of_lemmas, lemma_to_keywordbag = user_twitter_list_bag_of_words(twitter_lists_corpus,
sent_tokenize, _treebank_word_tokenize,
tagger, lemmatizer, lemmatize, stopset,
first_cap_re, all_cap_re, digits_punctuation_whitespace_re,
pos_set)
user_annotation = dict()
user_annotation["bag_of_lemmas"] = bag_of_lemmas
user_annotation["lemma_to_keywordbag"] = lemma_to_keywordbag
target_path = next(target_path_list)
with open(target_path, "w", encoding="utf-8") as fp:
json.dump(user_annotation, fp)
def worker_function(file_name_list, lemmatizing, source_folder, target_folder):
source_path_list = (source_folder + "/" + file_name for file_name in file_name_list)
target_path_list = (target_folder + "/" + file_name[:-4] + ".json" for file_name in file_name_list)
# Get the lists of a user
for source_path in source_path_list:
twitter_lists_corpus = load_pickle(source_path)
if "lists" in twitter_lists_corpus.keys():
twitter_lists_corpus = twitter_lists_corpus["lists"]
else:
continue
bag_of_lemmas, lemma_to_keywordbag = user_twitter_list_bag_of_words(twitter_lists_corpus, lemmatizing)
user_annotation = dict()
user_annotation["bag_of_lemmas"] = bag_of_lemmas
user_annotation["lemma_to_keywordbag"] = lemma_to_keywordbag
target_path = next(target_path_list)
with open(target_path, "w", encoding="utf-8") as fp:
json.dump(user_annotation, fp)
def make_annotation(
twitter_lists_folder,
twitter_lists_keywords_folder,
weakly_connected_graph_folder,
weakly_connected_label_folder,
full_graph_folder,
):
# TODO: Move keywords from Mongo to the folder.
# Read set of users.
weakly_connected_user_id_set = load_pickle(weakly_connected_graph_folder + "/user_id_set" + ".pkl")
weakly_connected_node_to_id = load_pickle(weakly_connected_graph_folder + "/node_to_id" + ".pkl")
id_to_name = load_pickle(full_graph_folder + "/id_to_name" + ".pkl")
# Read set of twitter lists.
twitter_list_file_list = os.listdir(twitter_lists_folder)
twitter_list_file_list = [int(file_name[:-4]) for file_name in twitter_list_file_list]
# Read which users are annotated.
user_keywords_file_list = os.listdir(twitter_lists_keywords_folder)
user_keywords_file_list = [int(file_name[:-5]) for file_name in user_keywords_file_list]
# Find which twitter lists need to be preprocessed.
user_twitter_id_list = [
file_name for file_name in twitter_list_file_list if file_name in weakly_connected_user_id_set
]
user_twitter_id_list = [file_name for file_name in user_twitter_id_list if file_name not in user_keywords_file_list]
twitter_list_file_list = [str(file_name) + ".pkl" for file_name in user_twitter_id_list]
pool = Pool(processes=get_threads_number() * 2)
user_chunks = chunks(twitter_list_file_list, get_threads_number() * 2)
pool.map(
partial(
worker_function,
lemmatizing="wordnet",
source_folder=twitter_lists_folder,
target_folder=twitter_lists_keywords_folder,
),
user_chunks,
)
# # Make user-label matrix.
user_keywords_file_list = [str(file_name) for file_name in user_keywords_file_list]
user_twitter_list_keywords_gen = read_local_user_annotations(twitter_lists_keywords_folder, user_keywords_file_list)
weakly_connected_id_to_node = dict(zip(weakly_connected_node_to_id.values(), weakly_connected_node_to_id.keys()))
# # twitter_id_to_weakly_connected_node = {int(twitter_id): weakly_connected_id_to_node[int(twitter_id)] for twitter_id in user_keywords_file_list if int(twitter_id) in weakly_connected_id_to_node.keys()}
# node_twitter_list_keywords_gen = ((weakly_connected_id_to_node[int(user_twitter_id)], twitter_list_keywords) for user_twitter_id, twitter_list_keywords in user_twitter_list_keywords_gen if int(user_twitter_id) in weakly_connected_id_to_node.keys())
# for node, j in user_twitter_list_keywords_gen:
# print(node, j)
implicated_user_twitter_list_keywords_gen = (
(int(user_twitter_id), twitter_list_keywords)
for user_twitter_id, twitter_list_keywords in user_twitter_list_keywords_gen
if int(user_twitter_id) in weakly_connected_id_to_node.keys()
)
# for node, j in user_twitter_list_keywords_gen:
# print(node, j)
####################################################################################################################
# Semi-automatic user annotation.
####################################################################################################################
reveal_set = get_reveal_set()
topic_keyword_dict = get_topic_keyword_dictionary()
available_topics = set(list(topic_keyword_dict.keys()))
keyword_list = list()
for topic in reveal_set:
if topic in available_topics:
keyword_list.extend(topic_keyword_dict[topic])
lemma_set = list()
for keyword in keyword_list:
lemma = clean_single_word(keyword, lemmatizing="wordnet")
lemma_set.append(lemma)
lemma_set = set(lemma_set)
keyword_topic_dict = dict()
for topic, keyword_set in topic_keyword_dict.items():
for keyword in keyword_set:
keyword_topic_dict[keyword] = topic
user_label_matrix, annotated_nodes, label_to_lemma, node_to_lemma_tokeywordbag = form_user_term_matrix(
implicated_user_twitter_list_keywords_gen,
weakly_connected_id_to_node,
lemma_set=lemma_set,
keyword_to_topic_manual=keyword_topic_dict,
)
scipy_sparse_to_csv(
weakly_connected_label_folder + "/unfiltered_user_label_matrix" + ".tsv", user_label_matrix, "\t", directed=True
)
store_pickle(weakly_connected_label_folder + "/unfiltered_annotated_nodes" + ".pkl", annotated_nodes)
store_pickle(weakly_connected_label_folder + "/unfiltered_label_to_lemma" + ".pkl", label_to_lemma)
store_pickle(
weakly_connected_label_folder + "/unfiltered_node_to_lemma_tokeywordbag" + ".pkl", node_to_lemma_tokeywordbag
)
user_label_matrix, annotated_user_ids, label_to_lemma = filter_user_term_matrix(
user_label_matrix, annotated_nodes, label_to_lemma, max_number_of_labels=None
)
lemma_to_keyword = form_lemma_tokeyword_map(annotated_nodes, node_to_lemma_tokeywordbag)
# user_label_matrix, annotated_user_ids, label_to_lemma, lemma_to_keyword = semi_automatic_user_annotation(implicated_user_twitter_list_keywords_gen, weakly_connected_id_to_node)
# Store user-label binary matrix.
scipy_sparse_to_csv(
weakly_connected_label_folder + "/user_label_matrix" + ".tsv", user_label_matrix, "\t", directed=True
)
# Store user-label keyword matrix.
write_screen_name_to_topics(
weakly_connected_label_folder + "/user_name_to_topics" + ".tsv",
user_label_matrix,
weakly_connected_node_to_id,
id_to_name,
label_to_lemma,
lemma_to_keyword,
separator="\t",
)
return twitter_lists_folder
def weakly_connected_graph(full_graph_folder, weakly_connected_graph_folder):
# Read relevant data.
mention_graph = load_pickle(full_graph_folder + "/mention_graph" + ".pkl")
mention_graph = spsp.coo_matrix(spsp.csr_matrix(mention_graph))
retweet_graph = load_pickle(full_graph_folder + "/retweet_graph" + ".pkl")
retweet_graph = spsp.coo_matrix(spsp.csr_matrix(retweet_graph))
user_lemma_matrix = load_pickle(full_graph_folder + "/user_lemma_matrix" + ".pkl")
user_lemma_matrix = spsp.coo_matrix(spsp.csr_matrix(user_lemma_matrix))
user_id_set = load_pickle(full_graph_folder + "/user_id_set" + ".pkl")
node_to_id = load_pickle(full_graph_folder + "/node_to_id" + ".pkl")
# Extract weakly connected graph for the mention graph.
weakly_connected_men_ret_graph, weakly_connected_node_to_id, old_node_list = extract_connected_components(
spsp.coo_matrix(spsp.csr_matrix(mention_graph + retweet_graph)), "weak", node_to_id
)
# Calculate the user twitter id set for the weakly connected component.
weakly_connected_user_id_set = set(list(weakly_connected_node_to_id.values()))
node_array = np.array(old_node_list, dtype=np.int64)
# Extract corresponding retweet graph and user lemma matrix.
weakly_connected_mention_graph = submatrix_pull_via_networkx(
spsp.coo_matrix(mention_graph), node_array, directed=True
)
weakly_connected_retweet_graph = submatrix_pull_via_networkx(
spsp.coo_matrix(retweet_graph), node_array, directed=True
)
user_lemma_matrix = spsp.csr_matrix(user_lemma_matrix)
weakly_connected_user_lemma_matrix = user_lemma_matrix[node_array, :]
# Change sparse matrices to coordinate format in order to save as an edge list.
weakly_connected_mention_graph = spsp.coo_matrix(weakly_connected_mention_graph)
weakly_connected_retweet_graph = spsp.coo_matrix(weakly_connected_retweet_graph)
weakly_connected_user_lemma_matrix = spsp.coo_matrix(weakly_connected_user_lemma_matrix)
# Store weakly connected data.
scipy_sparse_to_csv(
weakly_connected_graph_folder + "/mention_graph.tsv",
weakly_connected_mention_graph,
separator="\t",
directed=True,
)
scipy_sparse_to_csv(
weakly_connected_graph_folder + "/retweet_graph.tsv",
weakly_connected_retweet_graph,
separator="\t",
directed=True,
)
scipy_sparse_to_csv(
weakly_connected_graph_folder + "/user_lemma_matrix.tsv",
weakly_connected_user_lemma_matrix,
separator="\t",
directed=True,
)
store_pickle(weakly_connected_graph_folder + "/user_id_set" + ".pkl", weakly_connected_user_id_set)
store_pickle(weakly_connected_graph_folder + "/node_to_id" + ".pkl", weakly_connected_node_to_id)
