Sentiment Analysis using Word2vec-CNN-BiLSTM Classification

被引：10

作者：

Yue, Wang ^{[1
]}

Li, Lei ^{[2
]}

机构：

[1] Hosei Univ, Grad Sch Sci Engn, Li Lab, 3-7-2 Kajinocho, Koganei, Tokyo 1848584, Japan

[2] Hosei Univ, Dept Sci & Engn, 3-7-2 Kajinocho, Koganei, Tokyo 1848584, Japan

来源：

2020 SEVENTH INTERNATIONAL CONFERENCE ON SOCIAL NETWORK ANALYSIS, MANAGEMENT AND SECURITY (SNAMS) | 2020年

关键词：

sentiment analysis; CNN; BiLSTM; Word2vec; text classification;

D O I：

10.1109/SNAMS52053.2020.9336549

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Traditional neural network based short text classification algorithms for sentiment classification is easy to find the errors. In order to solve this problem, the Word Vector Model (Word2vec), Bidirectional Long-term and Short-term Memory networks (BiLSTM) and convolutional neural network (CNN) are combined. The experiment shows that the accuracy of CNN-BiLSTM model associated with Word2vec word embedding achieved 91.48%. This proves that the hybrid network model performs better than the single structure neural network in short text.

引用

页码：35 / 39

页数：5

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