利用产品评论进行情感分析的混合 HAN-CNN 与方面词提取技术

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
P. C. D. Kalaivaani, K. Sathyarajasekaran, N. Krishnamoorthy, T. Kumaravel
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引用次数: 0

摘要

本文利用产品评论提出了一种密集情感分析方法,称为分层注意力-卷积神经网络(HAN-CNN)。首先,对输入的产品评论进行双向变换器编码器表征(BERT)标记化处理,将每个句子的输入数据分割成单词的小比特。然后,进行方面术语提取(ATE),并利用一些特征完成特征提取。最后,情感分析由开发的 HAN-CNN 完成,HAN-CNN 由分层注意力网络(HAN)和卷积神经网络(CNN)组合而成。此外,所提出的 HAN-CNN 取得了更高的性能,最高准确率、召回率和 F1-Score 分别为 91.70%、90.60% 和 91.20%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid HAN-CNN with aspect term extraction for sentiment analysis using product review

In this article, an intensive sentiment analysis approach termed Hierarchical attention-convolutional neural network (HAN-CNN) has been proposed using product reviews. Firstly, the input product review is subjected to Bidirectional Encoder Representation from Transformers (BERT) tokenization, where the input data of each sentence are partitioned into little bits of words. Thereafter, Aspect Term Extraction (ATE) is carried out and feature extraction is completed utilizing some features. Finally, sentiment analysis is accomplished by the developed HAN-CNN, which is formed by combining a Hierarchical Attention Network (HAN) and a Convolutional Neural Network (CNN). Moreover, the proposed HAN-CNN achieved a greater performance with maximum accuracy, recall and F1-Score of 91.70%, 90.60% and 91.20%, respectively.

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来源期刊
Computational Intelligence
Computational Intelligence 工程技术-计算机:人工智能
CiteScore
6.90
自引率
3.60%
发文量
65
审稿时长
>12 weeks
期刊介绍: This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.
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