The everyday consumption of household goods is a significant source of environmental pollution. As people increasingly shop online, this affords an opportunity to provide consumers with actionable feedback on the social and environmental impact of potential purchases. In our work, we explore the following questions on Amazon a) do consumers bring up the environment in their reviews either directly or through relevant related topics? b) do they tend to bring up the environment when they are satisfied or dissatisfied with a product? c) in what granular context do they bring up the environment?

To address these questions, we designed an annotation task and recruited knowledgeable students to annotate consumer product reviews. This dataset comprises annotations for 779 individual reviews, with each review corresponding to a distinct product.

In our paper, we propose a machine learning method using these annotations that can discover signals of sustainability and infer a product's sustainability score. Our model and code are released at https://github.com/Sabina321/sustainable_signals. The data is for the following paper: Tong Lin, Tianliang Xu, Amit Zac, and Sabina Tomkins. SUSTAINABLESIGNALS: An AI Approach for Inferring Consumer Product Sustainability. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023. AI and Social Good Track.