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.

翻译：情感分析是自然语言处理领域的关键任务，涵盖文本级情感极性分类和词级词性情感极性判定。此类分析要求模型既能整体理解文本，又能提取细粒度信息。随着大语言模型的兴起，情感分析迎来了新的发展路径。本文提出通过利用单词与整体文本之间的相互增强效应来提升性能，深入探讨了词极性如何影响语篇的整体情感倾向。为支持研究，我们在现有情感分类数据集的基础上，标注了四个新型情感文本分类与词性数据集。此外，我们开发了参数量为70亿的通用情感分析模型。实验结果表明，我们的模型在所有四个数据集上的表现均优于gpt-3.5-turbo，凸显了相互增强效应在情感分析中的重要性。