Understanding Moral Preferences Using Sentiment Analysis

Capraro, Valerio and Vanzo, Andrea
(May 28, 2018).

Abstract

Behavioral scientists have shown that people are not solely motivated by the economic consequences of the available actions, but they also care about the actions themselves. Several models have been proposed to formalize this preference for "doing the right thing". However, a common limitation of these models is their lack of predictive power: given a set of instructions of a decision problem, they lack to make clear predictions of people's behavior. Here, we show that, at least in simple cases, the overall qualitative pattern of behavior can be predicted reasonably well using a Computational Linguistics technique, known as Sentiment Analysis. The intuition is that people are reluctant to make actions that evoke negative emotions, and are eager to make actions that stimulate positive emotions. To show this point, we conduct an economic experiment in which decision-makers either get 50 cents, and another person gets nothing, or the opposite, the other person gets 50 cents and the decision maker gets nothing. We experimentally manipulate the wording describing the available actions using six words, from very negative (e.g., stealing) to very positive (e.g., donating) connotations. In agreement with our theory, we show that sentiment polarity has a U-shaped effect on pro-sociality. We also propose a utility function that can qualitatively predict the observed behavior, as well as previously reported framing effects. Our results suggest that building bridges from behavioral sciences to Computational Linguistics can help improve our understanding of human decision making.

The research is here.