  Sentiment analysis is a pivotal task in the domain of natural language
processing. It encompasses both text-level sentiment polarity classification
and word-level Part of Speech(POS) sentiment polarity determination. Such
analysis challenges models to understand text holistically while also
extracting nuanced information. With the rise of Large Language Models(LLMs),
new avenues for sentiment analysis have opened. This paper proposes enhancing
performance by leveraging the Mutual Reinforcement Effect(MRE) between
individual words and the overall text. It delves into how word polarity
influences the overarching sentiment of a passage. To support our research, we
annotated 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 a
7-billion parameter size. Experimental results revealed that our model
surpassed the performance of gpt-3.5-turbo across all four datasets,
underscoring the significance of MRE in sentiment analysis.
