基于长短时记忆网络的新生代企业家思想极性分析研究  

Research on Ideological Polarity Analysis of New-generation Entrepreneurs Based on Long Short-Term Memory Network

作　　者：刘星 王一海[1] Liu Xing;Wang Yihai(School of Digital Commerce,Nanjing Vocational College of Information Technology,Nanjing 210023,China)

机构地区：[1]南京信息职业技术学院数字商务学院,南京210023

出　　处：《信息化研究》2025年第2期29-34,42,共7页INFORMATIZATION RESEARCH

基　　金：江苏高校哲学社会科学研究一般项目(No.2023SJYB0744);2024年度江苏省社科应用研究精品工程课题(24SYC-146);江苏高校“青蓝工程”。

摘　　要：本文聚焦于网络平台中短文本在舆情监控与评价分析中的核心地位,深入探讨了短文本思想极性分析所面临的挑战与难题。为提升分析准确性和效率,本文致力于开发一种针对短文本的高效思想极性分析技术。通过结合自然语言处理(NLP)与机器学习算法,特别是深度学习中的长短时记忆网络(LSTM)模型,实现了对短文本中正面、负面或中性思想倾向的自动识别和分类。实验结果表明,本文提出的基于短文本的思想极性分析研究方法展现了出色的性能,能够有效捕捉短文本中的情感倾向,并在准确率、召回率及F1值等关键指标上取得显著提升。这一技术不仅克服了短文本长度有限、信息稀疏等难题,还充分利用了LSTM模型在处理复杂文本特征与时序依赖方面的优势,有效提升了分析结果的准确性和可靠性。该技术对舆情监控、社会心理分析及企业家思想动态研判等领域具有重要应用价值。通过实时监测网络平台上的短文本信息,可以快速掌握公众情绪变化、社会热点趋势及新生代企业家思想动态,为政府决策、企业管理和市场研究提供有力数据支持和参考依据。This study emphasizes the pivotal role of short texts in public opinion monitoring and evaluative analysis within online platforms.It delves deeply into the challenges faced in sentiment polarity analysis of short texts.To address these challenges and enhance the accuracy and efficiency of analysis,this research is dedicated to developing an advanced sentiment polarity analysis technique specifically for short texts.By integrating Natural Language Processing(NLP)with machine learning algorithms,particularly the Long Short-Term Memory(LSTM)model from deep learning,this study achieves automatic recognition and classification of positive,negative,or neutral sentiment orientations in short texts.Experimental results demonstrate that the proposed short text-based sentiment polarity analysis method exhibits exceptional performance,effectively capturing sentiment tendencies in short texts and achieving substantial improvements in key metrics such as accuracy,recall,and F1 score.This technique not only addresses the limitations posed by the short length and sparse information of short texts but also leverages the strengths of the LSTM model in processing complex textual features and temporal dependencies,thereby significantly enhancing the accuracy and reliability of analysis results.This technology holds substantial applied value in areas such as public opinion monitoring,social psychological analysis,and the dynamic judgment of entrepreneurs'thoughts.By monitoring short text information on online platforms in real-time,it provides valuable insights into changes in public sentiment,social trending topics,and the ideological dynamics of new-generation entrepreneurs,offering robust data support and reference for government decision-making,corporate management,and market research.

关 键 词：短文本 思想极性 动态研判 新生代 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]