EMNLP 2019 short paper
Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks
Code is edited based on https://github.com/wxue004cs/GCAE
Environment: Python 3.6 Pytorch 0.4.0 torchtext 0.3.1 numpy 1.16.2 sacremoses 0.0.7 nltk 3.3.0
Pretrained Word Embedding: download the file glove.840B.300d.txt and put it under /embedding folder http://nlp.stanford.edu/data/glove.840B.300d.zip
Get word_list:
#encoding: utf-8
import os
def get_glove_words(glove_file_path):
if not os.path.exists(glove_file_path):
print(glove_file_path + ' not exists!')
return []
words_list = []
with open(glove_file_path, 'r', encoding="utf-8") as fo:
for line in fo:
word = line.encode().decode('utf-8').strip().split(' ')[0]
#print(word)
words_list.append(word)
return words_listProduce "glove_words.txt" file in /embedding folder:
words_list = get_glove_words('glove.840B.300d.txt')
print(len(words_list))
with open('glove_words.txt', 'w', encoding="utf-8") as fo:
for w in words_list:
fo.write(w+'\n')Run: Enter /model_files/ run train_gcae.sh run train_ataelstm.sh run train_datgru.sh
If our code helps, please cite:
@inproceedings{tan-etal-2019-recognizing,
title = "Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks",
author = "Tan, Xingwei and
Cai, Yi and
Zhu, Changxi",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-1342",
doi = "10.18653/v1/D19-1342",
pages = "3417--3422",
}