Improving sentiment analysis accuracy with emoji embedding

作     者：Chuchu Liu Fan Fang Xu Lin Tie Cai Xu Tan Jianguo Liu Xin Lu 

作者机构：College of Systems EngineeringNational University of Defense TechnologyChangsha410073China College of ComputerNational University of Defense TechnologyChangsha410073China School of Software EngineeringShenzhen Institute of Information TechnologyShenzhen518172China Institute of Accounting and FinanceShanghai University of Finance and EconomicsShanghai200433China 

出 版 物：《Journal of Safety Science and Resilience》 (安全科学与韧性（英文）)

年 卷 期：2021年第2卷第4期

页      面：246-252页

核心收录：

学科分类：1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 081203[工学-计算机应用技术] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the National Natural Sci-ence Foundation of China(82041020,72088101,91846301) XL ac-knowledges support from the National Natural Science Foundation of China(72025405,71771213) the Hunan Science and Technol-ogy Plan Project(2020JJ4673,2020TP1013) JL was supported by the National Natural Science Foundation of China(61773248) the Major Program of National Fund of Philosophy and Social Sci-ence of China(20ZDA060) TC and XT were supported by the Shen-zhen Basic Research Project for Development of Science and Technology(JCYJ20200109141218676). 

主　　题：Sentiment analysis Emoji CEmo-LSTM Sentiment evolution COVID-19 

摘      要：Due to the diversity and variability of Chinese syntax and semantics,accurately identifying and distinguishing individual emotions from online texts is challenging.To overcome this limitation,we incorporate a new source of individual sentiment,emojis,which contain thousands of graphic symbols and are increasingly being used for expressing emotion in online conversations.We examined popular sentiment analysis algorithms,including rule-based and classification algorithms,to evaluate the impact of supplementing emojis as additional features to improve the algorithm performance.Emojis were also translated into corresponding sentiment words when con-structing features for comparison with those directly generated from emoji label words.In addition,considering different functions of emojis in texts,we classified all posts in the dataset by their emoji usage and examined the changes in algorithm performance.We found that emojis are effective as expanding features for improving the accuracy of sentiment analysis algorithms,and the algorithm performance can be further increased by taking different emoji usages into consideration.In this study,we developed an improved emoji-embedding model based on Bi-LSTM(namely,CEmo-LSTM),which achieves the highest accuracy(around 0.95)when analyzing online Chinese texts.We applied the CEmo-LSTM algorithm to a large dataset collected from Weibo from December 1,2019 to March 20,2020 to understand the sentiment evolution of online users during the COVID-19 pandemic.We found that the pandemic remarkably impacted individual sentiments and caused more passive emotions(e.g.,horror and sadness).Our novel emoji-embedding algorithm creatively combined emojis as well as emoji usage with the sentiment analysis model and can handle emotion mining tasks more effectively and efficiently.