Bidirectional Long Short-Term Memory for Sentiment Analysis of Chinese Product Reviews

被引：0
|
作者：
Zhang, Kai [1 ]
Song, Wei [1 ]
Liu, Lizhen [1 ]
Zhao, Xinlei [2 ]
Du, Chao [1 ]
机构：
[1] Capital Normal Univ, Informat & Engn Coll, Beijing 100048, Peoples R China
[2] Capital Normal Univ, Foreign Language Coll, Beijing 100048, Peoples R China
来源：
PROCEEDINGS OF 2019 IEEE 9TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2019) | 2019年
基金：
中国国家自然科学基金; 北京市自然科学基金;
关键词：
sentiment analysis; product reviews; Bi-LSTM;
D O I：
10.1109/iceiec.2019.8784560
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Sentiment analysis of online product reviews and other user generated contents is a meaningful research subject for its wide range of applications. Traditional feature-based methods suffer from the limited scope of insufficient features caused by too short length of text. Bidirectional Long Short-Term Memory(Bi-LSTM), a neural network that extracts features of text automated, broadly used in data processing and predictions. Our paper is to explore a way for Bi-LSTM to identify the emotional polarity of product reviews. Our experimental results demonstrate that our proposed method, compared with previously reported model, performs better in precision, recall and F-score evaluation on three reviews data sets respectively.
引用
收藏
页码：665 / 668
页数：4
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