The Dimensional ABSA (DimABSA) sharedtask extends traditional aspect-based sentimentanalysis from categorical polarity to continuousvalence–arousal (VA) prediction. We presentour system for all three subtasks: DimensionalAspect Sentiment Regression (DimASR),Dimensional Aspect Sentiment Triplet Extrac-tion (DimASTE), and Dimensional AspectSentiment Quad Prediction (DimASQP).Due to the cascading nature of the differentsubtasks, we built a modular interlockingpipeline that uses classical Machine Learningand NLP methods.Experiments across domains show consistentgains in regression accuracy and structuredextraction performance. Our results demon-strate the effectiveness of distribution-awareregression, retrieval-augmented generation, andcontrastive prototype learning for dimensionalsentiment analysis.