Developing a standardized protocol for computational sentiment analysis research using health-related social media data

J Am Med Inform Assoc. 2020 Dec 22;ocaa298. doi: 10.1093/jamia/ocaa298. Online ahead of print.

Abstract

Objective: Sentiment analysis is a popular tool for analyzing health-related social media content. However, existing studies exhibit numerous methodological issues and inconsistencies with respect to research design and results reporting, which could lead to biased data, imprecise or incorrect conclusions, or incomparable results across studies. This article reports a systematic analysis of the literature with respect to such issues. The objective was to develop a standardized protocol for improving the research validity and comparability of results in future relevant studies.

Materials and methods: We developed the Protocol of Analysis of senTiment in Health (PATH) based on a systematic review that analyzed common research design choices and how such choices were made, or reported, among eligible studies published 2010-2019.

Results: Of 409 articles screened, 89 met the inclusion criteria. A total of 16 distinctive research design choices were identified, 9 of which have significant methodological or reporting inconsistencies among the articles reviewed, ranging from how relevance of study data was determined to how the sentiment analysis tool selected was validated. Based on this result, we developed the PATH protocol that encompasses all these distinctive design choices and highlights the ones for which careful consideration and detailed reporting are particularly warranted.

Conclusions: A substantial degree of methodological and reporting inconsistencies exist in the extant literature that applied sentiment analysis to analyzing health-related social media data. The PATH protocol developed through this research may contribute to mitigating such issues in future relevant studies.

Keywords: Facebook; Instagram; Twitter; Web 2.0; computing methodologies [L01.224]; machine learning [G17.035.250.500]; natural language processing [L01.224.050.375.580]; reference standard [E05.978.808]; sentiment analysis; social media [L01.178.75]; user-generated content.