def get_word_network_graph(self,
centrality_dict,
mode="markers",
centrality_th=0.5,
weight_th=0.5,
ego_node_list=[],
node_size_rate=10,
edge_width_rate=10,
text_size_rate=10):
tv = TextVisualizer()
tv.set_plotly()
if len(ego_node_list) > 0:
edge_list = [(a, b, w) for (a, b), w in self.get_edge_list()
if w >= weight_th and (
a in ego_node_list or b in ego_node_list) and (
centrality_dict[a] >= centrality_th
and centrality_dict[b] >= centrality_th)]
else:
edge_list = [
(a, b, w) for (a, b), w in self.get_edge_list()
if w >= weight_th and (centrality_dict[a] >= centrality_th
and centrality_dict[b] >= centrality_th)
]
node_list = []
for a, b, c in edge_list:
#if c >= weight_th:
if a not in node_list:
node_list.append(a)
if b not in node_list:
node_list.append(b)
data_meta = {
"node_list": node_list,
"edge_list": edge_list,
"weight_dict": centrality_dict
}
graph_meta = {
"title": "Word Network Graph",
"height": 1000,
"width": 1000,
"weight_name": "Word Centrality",
}
return tv.draw_network(data_meta,
graph_meta,
mode=mode,
node_size_rate=node_size_rate,
edge_width_rate=edge_width_rate)
def get_word_network_graph(self, centrality_dict):
tv = TextVisualizer()
tv.set_plotly()
data_meta = {
"node_list": self.get_node_list(),
"edge_list": [(a, b, w) for (a, b), w in self.get_edge_list()],
"weight_dict": centrality_dict
}
graph_meta = {
"title": "Word Network Graph",
"height": 1000,
"width": 1000,
"weight_name": "Word Centrality",
}
return tv.draw_network(data_meta, graph_meta)
def get_wordcloud(self, weight_dict):
tv = TextVisualizer()
tv.set_plotly()
data_meta = {
"weight_dict": weight_dict,
}
graph_meta = {
"height": 1000,
"width": 1000,
"min_font_size": 10,
"max_font_size": 500,
"margin": 10,
"background_color": "white"
}
tv.draw_wordcloud(data_meta, graph_meta)
def draw_sentiment_parse(self, token_list, weight_list):
tv = TextVisualizer()
tv.set_plotly()
return tv.draw_sentence_attention(token_list, weight_list)
def draw_sentence_weight(self, sentence):
token_list, weight_list = self.get_weight(sentence)
weight_list = [w**3 for w in weight_list]
tv = TextVisualizer()
tv.set_plotly()
return tv.draw_sentence_attention(token_list, weight_list)