期刊论文

Long text aspect-level sentiment analysis based on text filtering and improved BERT

中文

计算机应用

1001-9081

2020

40

10

2838-2844

10.11772/j.issn.1001-9081.2020020164

本校为第一机构

方面级情感分析旨在分类出文本在不同方面的情感倾向。在长文本的方面级情感分析中,由于长文本存在的冗余和噪声问题,导致现有的方面级情感分析算法对于长文本中方面相关信息的特征提取不够充分,分类不精准;而在方面分层为粗粒度和细粒度方面的数据集上,现有的解决方案没有利用粗粒度方面中的信息。针对以上问题,提出基于文本筛选和改进BERT的算法TFN+BERT-Pair-ATT。该算法首先利用长短时记忆网络(LSTM)和注意力机制相结合的文本筛选网络(TFN)从长文本中直接筛选出与粗粒度方面相关的部分语句;然后将部分语句按次序进行组合,并与细粒度方面相结合输入至在BERT上增加注意力层的BERT-Pair-ATT中进...

Aspect-level sentiment analysis aims to classify the sentiment of text in different aspects. In the aspect-level sentiment analysis of long text,the existing aspect-level sentiment analysis algorithms do not fully extract the features of aspect related information in the long text due to the redundancy and noise problems,leading to low classification accuracy. On the datasets with coarse and fine aspects,existing solutions do not take advantage of the information in the coarse aspect. In view of the above problems,an algorithm named TFN+BERT-Pair-ATT was proposed based on text filtering and im...