Based on more than 280,000 newspaper articles published in North America, this study proposes an integrative machine learning framework to explore heterogeneous social sentiments over time. After retrieving and preprocessing articles containing the term “Chinese” from six mainstream newspapers, we identified major discussion topics and assigned articles to their corresponding topics via posterior probabilities estimated by using a novel Bayesian nonparametric model, the hierarchical Dirichlet process. We also employed a groundbreaking deep learning technique, bidirectional encoder representations from transformers, to assign a negative or positive sentiment score to each newspaper article, which was trained on binary-labeled movie reviews from the Internet Movie Database (IMDb). By combining state-of-the-art tools for topic modeling and sentiment analysis, we found an overall lack of consensus on whether sentiments in North America since 1978 were pro- or anti-Chinese. Moreover, the images of Chinese are highly topic specific: (1) sentiments across different topics show distinct trajectories over the period of study; (2) discussion topics explain much more of the variation in sentiments than do the publisher, year of publication, or country of publisher; (3) less positive sentiments appear to be more relevant to material concerns than to ethnic considerations, whereas more positive sentiments are associated with an appreciation of culture; and (4) sentiments on the same or similar topic might exhibit different temporal patterns in the United States and Canada. These new findings not only suggest a multifaceted and dynamic view of social sentiments in a transnational context but also call for a paradigm shift in understanding intertwined sociodiscursive interactions over time.