A Wide & Deep Learning Approach for Covid-19 Tweet Classification

被引：0

作者：

Valdes-Chavez, Alberto ^{[1
]}

Roberto Lopez-Santillan, J. ^{[1
]}

Carlos Gonzalez-Gurrola, L. ^{[1
]}

Ramirez-Alonso, Graciela ^{[1
]}

Montes-y-Gomez, Manuel ^{[2
]}

机构：

[1] Univ Autonoma Chihuahua, Fac Ingn, Circuito Univ Campus 2, Chihuahua 31125, Chihuahua, Mexico

[2] Inst Nacl Astrofis Opt & Elect INAOE, Coordinac Ciencias Computac, Luis Enrique Erro 1, Puebla 72840, Mexico

来源：

PATTERN RECOGNITION, MCPR 2022 | 2022年 / 13264卷

关键词：

Social media; Data mining; Natural language processing; Text classification; Wide & Deep; Covid-19;

D O I：

10.1007/978-3-031-07750-0_21

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Public health surveillance via social media can be a useful tool to identify and track potential cases of a disease. The aim of this research was to design a method for identifying tweets describing potential Covid-19 cases. The proposed method uses a Wide & Deep (W&D) architecture, which combines two learning branches fed from different features to improve classification effectiveness. The deep branch uses a BERT-type model, while the wide branch considers two different lexical-based features. It was evaluated on the data from Task 5 of the Social Media Mining For Health (#SMM4H) 2021 competition. Results show that the proposed W&D method performed better than the wide-only and deep-only models, achieving an F1-score of 0.79 which matches the results of the 1st place ensemble-model.

引用

页码：225 / 234

页数：10

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