Entity-Centric Contextual Affective Analysis
This addresses the need for social-oriented NLP tasks by exploring affect analysis, but it is incremental as it applies existing methods to a new domain with noted limitations.
The paper tackled the problem of using contextualized word embeddings to capture affect dimensions in portrayals of people, finding that they encode meaningful affect information but are heavily biased towards training data, limiting usefulness to in-domain analyses, with results evaluated quantitatively on held-out lexicons and qualitatively through case examples.
While contextualized word representations have improved state-of-the-art benchmarks in many NLP tasks, their potential usefulness for social-oriented tasks remains largely unexplored. We show how contextualized word embeddings can be used to capture affect dimensions in portrayals of people. We evaluate our methodology quantitatively, on held-out affect lexicons, and qualitatively, through case examples. We find that contextualized word representations do encode meaningful affect information, but they are heavily biased towards their training data, which limits their usefulness to in-domain analyses. We ultimately use our method to examine differences in portrayals of men and women.