Abstract

Learning sentiment models from short texts such as tweets is a notoriously challenging problem due to very strong noise and data sparsity. This paper presents a novel, collaborative filtering-based approach for sentiment prediction in twitter conversation threads. Given a set of sentiment holders and sentiment targets, we assume we know the true sentiments for a small fraction of holder-target pairs. This information is then used to predict the sentiment of a previously unknown user towards another user or an entity using collaborative filtering algorithms. We validate our model on two Twitter datasets using different collaborative filtering techniques. Our preliminary results demonstrate that the proposed approach can be effectively used in twitter sentiment prediction, thus mitigating the data sparsity problem.

Date

March 16, 2026

Authors

Jihie Kim, Jaebong Yoo, Ho Lim, Huida Qiu, Zornitsa Kozareva, Aram Galstyan

Journal

Proceedings of the international AAAI conference on web and social media

Volume

7

Issue

1

Pages

685-688