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：摘要：情感分析是自然语言处理领域中的关键任务，既涵盖文本层面的情感极性分类，也包含词性（POS）层面的情感极性判定。此类分析要求模型在理解文本整体语义的同时，还能提取其细粒度信息。随着大型语言模型（LLMs）的兴起，情感分析迎来了新的研究路径。本文提出通过利用单个词汇与整体文本之间的互增强效应（MRE）来提升模型性能，深入探究词汇极性如何影响语篇的整体情感倾向。为支撑研究，我们在现有情感分类数据集基础上，标注了四个新型情感文本分类与词性（SCPOS）数据集，并开发了参数量为70亿的通用情感分析（USA）模型。实验结果表明，该模型在所有四个数据集上的表现均超越gpt-3.5-turbo，凸显了MRE在情感分析中的重要性。