Text sentiment classification based on BP neural network

被引：2
|
作者：
Cheng, Nanchang [1 ]
Soong, Wenchao [1 ]
Song, Kang [1 ]
机构：
[1] Commun Univ China, Natl Broadcast Media Language Resources Monitorin, State Key Lab OfMedia Convergence & Commun, Beijing, Peoples R China
来源：
2021 21ST ACIS INTERNATIONAL WINTER CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD-WINTER 2021) | 2021年
基金：
国家重点研发计划;
关键词：
BP neural network; Text emotion classification; Emotional analysis; Text classification;
D O I：
10.1109/SNPDWinter52325.2021.00010
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In a consumer-oriented market, people's opinions or comments will, directly or indirectly, have influence on other people's decisions. For example, as choosing goods or online services, positive opinions or comments can promote consumers' purchase At the same time, negative comments can also reduce consumers' enthusiasm for buying. The BP neural network will be trained with a supervised learning method, and finally obtain the ability that can classify positive or negative text reviews. In this paper, the supervised training method is adopted to improve the operation speed of the whole model by 8-9 times after optimizing the data and structure. The accuracy rate, recall rate, and F1 value are around 87.5%, 86. 7%, and 87.35% respectively.
引用
收藏
页码：1 / 4
页数：4
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