My Words Imply Your Opinion: Reader Agent-Based Propagation Enhancement for Personalized Implicit Emotion Analysis

In implicit emotion analysis (IEA), the subtlety of emotional expressions makes it particularly sensitive to user-specific characteristics. Existing studies often inject personalization into the analysis by focusing on the authorial dimension of the emotional text. However, these methods overlook the potential influence of the intended reader on the reaction of implicit emotions. In this paper, we refine the IEA task to Personalized Implicit Emotion Analysis (PIEA) and introduce the RAPPIE model, a novel framework designed to address the issue of missing user information within this task. In particular, 1) we create reader agents based on the Large Language Model to simulate reader reactions, to address challenges of the spiral of silence and data incompleteness encountered when acquiring reader feedback information. 2) We establish a reader propagation role system and develop a role-aware emotion propagation multi-view graph learning model, which effectively deals with the sparsity of reader information by utilizing the distribution of propagation roles. 3) We annotate two Chinese PIEA datasets with detailed user metadata, thereby addressing the limitation of prior datasets that primarily focus on textual content annotation. Extensive experiments on these datasets indicate that the RAPPIE model outperforms current state-of-the-art baselines, highlighting the significance and efficacy of incorporating reader feedback into the PIEA process.

RealityTalk: Real-Time Speech-Driven Augmented Presentation for AR Live Storytelling

We present RealityTalk, a system that augments real-time live presentations with speech-driven interactive virtual elements. Augmented presentations leverage embedded visuals and animation for engaging and expressive storytelling. However, existing tools for live presentations often lack interactivity and improvisation, while creating such effects in video editing tools require significant time and expertise. RealityTalk enables users to create live augmented presentations with real-time speech-driven interactions. The user can interactively prompt, move, and manipulate graphical elements through real-time speech and supporting modalities. Based on our analysis of 177 existing video-edited augmented presentations, we propose a novel set of interaction techniques and then incorporated them into RealityTalk. We evaluate our tool from a presenter's perspective to demonstrate the effectiveness of our system.