Evaluating the Effectiveness of Commonly Used Sentiment Analysis Models for the Second Indochina War

Sentiment analysis, a subfield of Natural Language Processing (NLP), is widely used to analyze public opinion, yet its application to historical and political nuanced events are unexplored. This study evaluates the effectiveness of two popular open-source sentiment analysis models—VADER, a lexicon-based approach, and DistilBERT, a transformer-based deep learning model—in analyzing texts related to a historical and politically nuanced event: The Second Indochina War, commonly known as the Vietnam War. Using a dataset categorized into Pro-Vietnamese, Pro-American, and Civilian perspectives, the models were assessed for their ability to capture emotional and ideological nuances. While VADER proved efficient for informal texts, it oversimplified complex narratives. DistilBERT captured subtle context but struggled with ideological undertones. Results highlight the challenges of applying existing sentiment models to historically significant data. The study emphasizes the need for hybrid approaches combining automated tools with human analysis to improve accuracy. Future work could expand datasets and incorporate advanced models for a deeper understanding of politically charged texts.