Neutral or Framed? A Sentiment Analysis of 2019 Abortion Laws

INTRODUCTION: This study employs sentiment analysis (SA) to examine the semantic structures of restrictive and protective abortion bills enacted in 2019. SA is a Natural Language Processing (NLP) technique that uses automation to extract affective indicators (emotive language) from text data. Assessing these indicators can help identify whether legal texts are framed, or intentionally biased in their wording. Identifying framing is important for understanding potentially biased interpretations of these laws. METHODS: We identified a sample of 2019 abortion bills using the legislative tracking tool Legiscan and included those that met specified criteria (N = 19 bills). We categorized each bill as restrictive (n = 12) or protective (n = 7). We ran aggregate (i.e., all bills) and separate (protective × restrictive) SA, generating scores that we interpreted qualitatively (higher scores indicated predominance of positive wording). RESULTS: In the aggregate analysis, 56% of text comprised negative terms (44% positive). Restrictive bills contained more negative language than protective bills (67% vs 58%). Although SA scores varied from −222 to +13, two laws scored 0, indicating neutrality. For comparison, the US Constitution’s score equaled 1. CONCLUSION: Our findings confirm SA is useful to examine legal documents for language biases. The abortion bills we assessed seem framed along political ideologies, although the sample provided evidence that neutral wording is possible. POLICY IMPLICATIONS: With the recent additions of conservative-leaning Justices to the US Supreme Court, Roe v. Wade is again at the center of partisan conflict. Thus, how abortion laws are framed draws further implications for how they may be interpreted when challenged in the court system.