A Deep Learning Model Based on Neural Bag-of-Words Attention for Sentiment Analysis

被引：0

作者：

Liao, Jing ^{[1
]}

Yi, Zhixiang ^{[1
]}

机构：

[1] Hunan Univ Sci & Technol, Sch Comp Engn & Sci, Xiangtan, Peoples R China

来源：

KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, PT I | 2021年 / 12815卷

关键词：

Sentiment analysis; Deep learning; Attention mechanism; Neural bag-of-words;

D O I：

10.1007/978-3-030-82136-4_38

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the field of Natural Language Processing, sentiment analysis is one of core research directions. The hot issue of sentiment analysis is how to avoid the shortcoming of using fixed vector to calculate attention distribution. In this paper, we proposed a novel sentiment analysis model based on neural bag-of-words attention, which utilizes Bidirectional Long Short-Term Memory (BiLSTM) to capture the deep semantic features of text, and fusion these features by attention distribution based on neural bag-of-words. The experimental results show that the proposed method has improved 2.53%-6.46% accuracy compared with the benchmark.

引用

页码：467 / 478

页数：12

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