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generate_sentic_graph.py
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generate_sentic_graph.py
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# -*- coding: utf-8 -*-
import numpy as np
#import spacy
import pickle
#nlp = spacy.load('en_core_web_sm')
def load_sentic_word():
"""
load senticNet
"""
path = './senticNet/senticnet_word.txt'
senticNet = {}
fp = open(path, 'r')
for line in fp:
line = line.strip()
if not line:
continue
word, sentic = line.split('\t')
senticNet[word] = sentic
fp.close()
return senticNet
def dependency_adj_matrix(text, aspect, senticNet):
word_list = text.split()
seq_len = len(word_list)
matrix = np.zeros((seq_len, seq_len)).astype('float32')
for i in range(seq_len):
word = word_list[i]
if word in senticNet:
sentic = float(senticNet[word]) + 1.0
else:
sentic = 0
if word in aspect:
sentic += 1.0
for j in range(seq_len):
matrix[i][j] += sentic
matrix[j][i] += sentic
for i in range(seq_len):
if matrix[i][i] == 0:
matrix[i][i] = 1
return matrix
def process(filename):
senticNet = load_sentic_word()
fin = open(filename, 'r', encoding='utf-8', newline='\n', errors='ignore')
lines = fin.readlines()
fin.close()
idx2graph = {}
fout = open(filename+'.sentic', 'wb')
for i in range(0, len(lines), 3):
text_left, _, text_right = [s.lower().strip() for s in lines[i].partition("$T$")]
aspect = lines[i + 1].lower().strip()
adj_matrix = dependency_adj_matrix(text_left+' '+aspect+' '+text_right, aspect, senticNet)
idx2graph[i] = adj_matrix
pickle.dump(idx2graph, fout)
print('done !!!', filename)
fout.close()
if __name__ == '__main__':
process('./datasets/acl-14-short-data/train.raw')
process('./datasets/acl-14-short-data/test.raw')
process('./datasets/semeval14/restaurant_train.raw')
process('./datasets/semeval14/restaurant_test.raw')
process('./datasets/semeval14/laptop_train.raw')
process('./datasets/semeval14/laptop_test.raw')
process('./datasets/semeval15/restaurant_train.raw')
process('./datasets/semeval15/restaurant_test.raw')
process('./datasets/semeval16/restaurant_train.raw')
process('./datasets/semeval16/restaurant_test.raw')