  In this paper, we present our advanced solutions to the two sub-challenges of
Affective Behavior Analysis in the wild (ABAW) 2023: the Emotional Reaction
Intensity (ERI) Estimation Challenge and Expression (Expr) Classification
Challenge. ABAW 2023 aims to tackle the challenge of affective behavior
analysis in natural contexts, with the ultimate goal of creating intelligent
machines and robots that possess the ability to comprehend human emotions,
feelings, and behaviors. For the Expression Classification Challenge, we
propose a streamlined approach that handles the challenges of classification
effectively. However, our main contribution lies in our use of diverse models
and tools to extract multimodal features such as audio and video cues from the
Hume-Reaction dataset. By studying, analyzing, and combining these features, we
significantly enhance the model's accuracy for sentiment prediction in a
multimodal context. Furthermore, our method achieves outstanding results on the
Emotional Reaction Intensity (ERI) Estimation Challenge, surpassing the
baseline method by an impressive 84\% increase, as measured by the Pearson
Coefficient, on the validation dataset.
