Sentiment analysis in public health: a systematic review of the current state, challenges, and future directions

Abstract

Introduction: Sentiment analysis, using natural language processing to understand opinions in text, is increasingly relevant for public health given the volume of online health discussions. Effectively using this approach requires understanding its methods, applications, and limitations. This systematic review provides a comprehensive overview of sentiment analysis in public health, examining methodologies, applications, data sources, challenges, evaluation practices, and ethical considerations.

Methods: We conducted a systematic review following PRISMA guidelines, searching academic databases through Semantic Scholar and screening studies for relevance. A total of 83 papers analyzing the use of sentiment analysis in public health contexts were included.

Results: The review identified a trend toward the use of advanced deep learning methods and large language models (LLMs) for a wide range of public health applications. However, challenges remain, particularly related to interpretability and resource demands. Social media is the predominant data source, which raises concerns about data quality, bias, linguistic complexity, and ethical issues.

Discussion: Sentiment analysis offers the potential for gaining public health insights but faces significant methodological, data-related, and ethical challenges. Reliable and ethical application demands rigorous validation, improved model interpretability, the development of ethical frameworks, and continued research to support responsible development and deployment.

Keywords: LLM; mental health; natural language processing; public health; sentiment analysis; systematic review.

Figure 1

PRISMA 2020 flow diagram illustrating the study identification, screening, eligibility, and inclusion process.

Figure 2

Frequency of the main sentiment analysis techniques reported in the 83 studies included in this review. Techniques are grouped into four categories: large language models (LLMs), machine learning (ML), deep learning (DL), and lexicon-based methods. Bars indicate the number of studies that applied each category, with example models/techniques shown alongside the corresponding frequency values (e.g., BERT, Naïve Bayes, LSTM, VADER).

Figure 3

Hierarchical distribution of the main data sources used across the 83 studies included in this review, all of which applied sentiment analysis in public health. The diagram illustrates the relationship between broad data source categories (e.g., Social Media, Survey Data, Clinical Data) and their specific instances (e.g., Twitter, Reddit, EHR data). Flow widths are proportional to the number of studies referencing each source. The numbers in parentheses next to each node indicate the frequency of use across the included studies, highlighting the predominance of social media platforms–particularly Twitter—as primary input data for sentiment analysis.

Figure 4

Frequency of main six evaluation metric categories reported across the 83 studies included in this review. Accuracy was the most commonly reported metric, followed by F1-score, precision, and recall. A smaller number of studies incorporated fairness- or engagement-related metrics, reflecting an emerging emphasis on ethical and participatory evaluation frameworks in public health sentiment analysis.