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Sentiment analysis is integral to understanding the voice of the customer andinforming businesses' strategic decisions. Conventional sentiment analysisinvolves three separate tasks: aspect-category detection (ACD), aspect-categorysentiment analysis (ACSA), and rating prediction (RP). However, independentlytackling these tasks can overlook their interdependencies and often requiresexpensive, fine-grained annotations. This paper introduces Unified SentimentAnalysis (Uni-SA), a novel learning paradigm that unifies ACD, ACSA, and RPinto a coherent framework. To achieve this, we propose the Distantly SupervisedPyramid Network (DSPN), which employs a pyramid structure to capture sentimentat word, aspect, and document levels in a hierarchical manner. Evaluations onmulti-aspect review datasets in English and Chinese show that DSPN, using onlystar rating labels for supervision, demonstrates significant efficiencyadvantages while performing comparably well to a variety of benchmark models.Additionally, DSPN's pyramid structure enables the interpretability of itsoutputs. Our findings validate DSPN's effectiveness and efficiency,establishing a robust, resource-efficient, unified framework for sentimentanalysis.

From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant Supervision