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Sentiment analysis is a pivotal task in the domain of natural languageprocessing. It encompasses both text-level sentiment polarity classificationand word-level Part of Speech(POS) sentiment polarity determination. Suchanalysis challenges models to understand text holistically while alsoextracting nuanced information. With the rise of Large Language Models(LLMs),new avenues for sentiment analysis have opened. This paper proposes enhancingperformance by leveraging the Mutual Reinforcement Effect(MRE) betweenindividual words and the overall text. It delves into how word polarityinfluences the overarching sentiment of a passage. To support our research, weannotated four novel Sentiment Text Classification and Part of Speech(SCPOS)datasets, building upon existing sentiment classification datasets.Furthermore, we developed a Universal Sentiment Analysis(USA) model, with a7-billion parameter size. Experimental results revealed that our modelsurpassed the performance of gpt-3.5-turbo across all four datasets,underscoring the significance of MRE in sentiment analysis.

USA: Universal Sentiment Analysis Model & Construction of Japanese Sentiment Text Classification and Part of Speech Dataset