Deep Learning Sentiment Classification Based on Weak Tagging Information

被引:13
作者
Wang, Chuantao
Yang, Xuexin [1 ]
Ding, Linkai
机构
[1] Beijing Univ Civil Engn & Architecture, Sch Mech Elect & Vehicle Engn, Beijing 102616, Peoples R China
关键词
Tagging; Data models; Task analysis; Deep learning; Dictionaries; Training; Cleaning; sentiment classification; weak tagging information; imbalanced classification;
D O I
10.1109/ACCESS.2021.3077059
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The purpose of sentiment classification is to solve the problem of automatic judgment of text sentiment tendency. In the sentiment classification task of online reviews, traditional deep learning sentiment classification models focus on algorithm optimization to improve the classification performance of the model, but when the sample data for manually labeling sentiment tendencies is insufficient, the classification performance of the model will be poor. The deep learning sentiment classification model based on weak tagging information, on the one hand, introduces weak tagging information into the training process of the model to reduce the use of manually tagging data. On the other hand, weak tagging information can represent the sentiment tendency of reviews to a certain extent, but it also contains noise, the model reduces the negative impact of the noise in weak tagging information in order to improve the classification performance of the sentiment classification model. The experimental results show that in the sentiment classification task of hotel online reviews, the deep learning sentiment classification model based on weak tagging information has superior classification performance than the traditional deep model without increasing labor cost.
引用
收藏
页码:66509 / 66518
页数:10
相关论文
共 50 条
[41]   Sentence-Level Sentiment Classification A Comparative Study between Deep Learning Models [J].
Mifrah S. ;
Benlahmar E.H. .
Journal of ICT Standardization, 2022, 10 (02) :339-352
[42]   Sentiment classification on product reviews using machine learning and deep learning techniques [J].
Singh, Neha ;
Jaiswal, Umesh Chandra .
INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT, 2024, 15 (12) :5726-5741
[43]   Review Sentiment Analysis Based on Deep Learning [J].
Hu, Zhongkai ;
Hu, Jianqing ;
Ding, Weifeng ;
Zheng, Xiaolin .
2015 IEEE 12TH INTERNATIONAL CONFERENCE ON E-BUSINESS ENGINEERING (ICEBE), 2015, :87-94
[44]   Sentiment Analysis and Classification Using Deep Semantic Information and Contextual Knowledge [J].
Al-Absi, Ahmed Abdulhakim ;
Kang, Dae-Ki ;
Al-Absi, Mohammed Abdulhakim .
CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 74 (01) :671-691
[45]   Information geometry enhanced fuzzy deep belief networks for sentiment classification [J].
Meng Wang ;
Zhen-Hu Ning ;
Tong Li ;
Chuang-Bai Xiao .
International Journal of Machine Learning and Cybernetics, 2019, 10 :3031-3042
[46]   Information geometry enhanced fuzzy deep belief networks for sentiment classification [J].
Wang, Meng ;
Ning, Zhen-Hu ;
Li, Tong ;
Xiao, Chuang-Bai .
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2019, 10 (11) :3031-3042
[47]   Deep learning-based natural language sentiment classification model for recognizing users' sentiments toward residential space [J].
Chang, Sun-Woo ;
Dong, Won-Hyeok ;
Rhee, Deuk-Young ;
Jun, Han-Jong .
ARCHITECTURAL SCIENCE REVIEW, 2021, 64 (05) :410-421
[48]   DEEP LEARNING BASED CLASSIFICATION USING SEMANTIC INFORMATION FOR POLSAR IMAGE [J].
Zhang, Lu ;
Xie, Wen ;
Zhao, Feng ;
Liu, Hanqiang ;
Duan, Yiping .
IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, :196-199
[49]   Research on Online Review Information Classification Based on Multimodal Deep Learning [J].
Liu, Jingnan ;
Sun, Yefang ;
Zhang, Yueyi ;
Lu, Chenyuan .
APPLIED SCIENCES-BASEL, 2024, 14 (09)
[50]   Deep Learning-based Sentiment Classification in Amharic using Multi-lingual Datasets [J].
Tesfagergish, Senait Gebremichael ;
Damasevicius, Robertas ;
Kapociute-Dzikiene, Jurgita .
COMPUTER SCIENCE AND INFORMATION SYSTEMS, 2023, 20 (04) :1459-1481