This paper proposes a smart trash bin to promote pro-environmental attitudes and behavior. The proposed system automatically searches for and displays news articles that are relevant to the type of trash discarded. The device ranks news articles in consideration of both the discarded trash type and its potential to evoke a sense of environmental urgency or guilt. To achieve this ranking, we constructed a dataset of news articles annotated for their “crisis/guilt” sentiment and trained a ranking model using a learning-to-rank algorithm. The highest-ranked article is displayed on a screen installed in the device. The results of a user study demonstrated that a prototype device presented an opportunity for participants to reflect on environmental issues and demonstrated a modest influence on the formation of pro-environmental attitudes.