Abstract
In the age of social media, understanding public sentiment toward major corporations is crucial for investors, policymakers, and researchers. This paper presents a comprehensive sentiment analysis system tailored for corporate reputation monitoring, combining Natural Language Processing (NLP) and machine learning techniques to accurately interpret public opinion in real time. The methodology integrates a hybrid sentiment detection framework leveraging both rule-based models (VADER) and transformer-based deep learning models (DistilBERT), applied to social media data from multiple platforms. The system begins with robust preprocessing involving noise removal and text normalization, followed by sentiment classification using an ensemble approach to ensure both interpretability and contextual accuracy. Results are visualized through sentiment distribution plots, comparative analyses, and temporal sentiment trends for enhanced interpretability. Our analysis reveals significant disparities in public sentiment across major corporations, with companies like Amazon (81.2) and Samsung (45.8) receiving excellent sentiment scores, while Microsoft (21.7) and Walmart (21.9) exhibit poor sentiment profiles. These findings demonstrate the utility of our multi-source sentiment framework in providing actionable insights regarding corporate public perception, enabling stakeholders to make informed strategic decisions based on comprehensive sentiment analysis.
Abstract (translated)
在社交媒体时代，理解公众对大型企业的情绪对于投资者、政策制定者和研究人员来说至关重要。本文提出了一种全面的情感分析系统，该系统专门用于企业声誉监测，并结合自然语言处理（NLP）和机器学习技术来实时准确地解读公众意见。 本研究的方法论整合了一个混合情感检测框架，利用基于规则的模型（VADER）和基于转换器的深度学习模型（DistilBERT），应用于来自多个平台的社会媒体数据。该系统首先进行强大的预处理步骤，包括去除噪声和文本规范化，然后使用集成方法进行情感分类，以确保可解释性和上下文准确性。结果通过情感分布图、比较分析以及时间趋势来可视化，从而增强其可解释性。 我们的分析揭示了大型企业之间公众情绪的重大差异：亚马逊（81.2）和三星（45.8）获得了出色的情感评分，而微软（21.7）和沃尔玛（21.9）则表现出较低的情绪评分。这些发现证明了我们多源情感框架的实用性，在提供有关公司公共形象可操作见解方面具有重要意义，使利益相关者能够根据全面的情感分析做出明智的战略决策。
URL
https://arxiv.org/abs/2504.15448
