Detecting Sentiment toward Emerging Infectious Diseases on Social Media: A Validity Evaluation of Dictionary-Based Sentiment Analysis

Sanguk Lee; Siyuan Ma; Jingbo Meng; Jie Zhuang; Tai-Quan Peng

doi:10.3390/ijerph19116759

Detecting Sentiment toward Emerging Infectious Diseases on Social Media: A Validity Evaluation of Dictionary-Based Sentiment Analysis

Int J Environ Res Public Health. 2022 Jun 1;19(11):6759. doi: 10.3390/ijerph19116759.

Authors

Sanguk Lee¹, Siyuan Ma¹, Jingbo Meng¹, Jie Zhuang², Tai-Quan Peng¹

Affiliations

¹ Department of Communication, Michigan State University, East Lansing, MI 48824, USA.
² Bob Schieffer College of Communication, Texas Christian University, Fort Worth, TX 76129, USA.

Abstract

Despite the popularity and efficiency of dictionary-based sentiment analysis (DSA) for public health research, limited empirical evidence has been produced about the validity of DSA and potential harms to the validity of DSA. A random sample of a second-hand Ebola tweet dataset was used to evaluate the validity of DSA compared to the manual coding approach and examine the influences of textual features on the validity of DSA. The results revealed substantial inconsistency between DSA and the manual coding approach. The presence of certain textual features such as negation can partially account for the inconsistency between DSA and manual coding. The findings imply that scholars should be careful and critical about findings in disease-related public health research that use DSA. Certain textual features should be more carefully addressed in DSA.

Keywords: ANEW; LIWC; SentiWordNet; infectious diseases; sentiment analysis; validity.

MeSH terms

Attitude
Communicable Diseases, Emerging*
Humans
Public Health
Sentiment Analysis
Social Media*

Grants and funding

This research received no external funding.