NLP

"That's so cute!": The CARE Dataset for Affective Response Detection

December 06, 2022

Abstract

Social media plays an increasing role in our communication with friends and family, and in our consumption of entertainment and information. Hence, to design effective ranking functions for posts on social media, it would be useful to predict the affective responses of a post (e.g., whether it is likely to elicit feelings of entertainment, inspiration, or anger). Similar to work on emotion detection (which focuses on the affect of the publisher of the post), the traditional approach to recognizing affective response would involve an expensive investment in human annotation of training data. We create and publicly release CARE DB, a dataset of 230k social media post annotations according to seven affective responses using the Common Affective Response Expression (CARE) method. The CARE method is a means of leveraging the signal that is present in comments that are posted in response to a post, providing high-precision evidence about the affective response to the post without human annotation. Unlike human annotation, the annotation process we describe here can be iterated upon to expand the coverage of the method, particularly for new affective responses. We present experiments that demonstrate that the CARE annotations compare favorably with crowdsourced annotations. Finally, we use CARE DB to train competitive BERT-based models for predicting affective response as well as emotion detection, demonstrating the utility of the dataset for related tasks.

Download the Paper

AUTHORS

Written by

Jane Yu

Alon Halevy

Publisher

CoNLL

Related Publications

February 21, 2024

INTEGRITY

NLP

Watermarking Makes Language Models Radioactive

Tom Sander, Pierre Fernandez, Alain Durmus, Matthijs Douze, Teddy Furon

February 21, 2024

December 07, 2023

CONVERSATIONAL AI

NLP

Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations

Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Davide Testuggine, Madian Khabsa

December 07, 2023

December 06, 2023

NLP

Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings

Mattia Atzeni, Mike Plekhanov, Frederic Dreyer, Nora Kassner, Simone Merello, Louis Martin, Nicola Cancedda

December 06, 2023

December 04, 2023

NLP

PATHFINDER: Guided Search over Multi-Step Reasoning Paths

Olga Golovneva, Sean O'Brien, Ram Pasunuru, Tianlu Wang, Luke Zettlemoyer, Maryam Fazel-Zarandi, Asli Celikyilmaz

December 04, 2023

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.