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.

Cet article explore les excursions dans le temps et leurs implications.

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