基于BERT、LSTM和认知词典的情感分析

Hsiao-Ting Tseng, Y. Zheng, Chen-Chiung Hsieh
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引用次数: 2

摘要

由于疫情,为了大大降低面对面访谈的感染风险,本文实现了BERT结合RCNN对文本的正反方向进行判断,然后利用BERT的下一句预测(NSP)找出文本中与主题相关的句子。最后,使用认知词典来计算同意或不同意的程度,从而得到审稿人的支持程度。这篇论文对于让访问者或作者知道受访者的观点是什么也很有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentiment Analysis using BERT, LSTM, and Cognitive Dictionary
Due to the epidemic situation, in order to greatly reduce the infection risk of face-to-face interviews, this paper implements the BERT combined with RCNN to judge the positive and negative directions of the text, and then uses BERT's next sentence prediction (NSP) to find out the topic-related sentences in the text. Finally, a cognitive dictionary is used to calculate the degree of agreement or disagreement, so as to obtain the degree of support of the reviewer. This paper is also useful for letting visitors or authors know what the respondents' views are.
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