Exploring the Emotional Impact of Layoffs: A Twitter-Based Sentiment Analysis with NLP Techniques

Employee layoffs have significant emotional and psychological impacts, often reflected in public discussions on social media platforms like Twitter. Employee layoffs not only affect the employee at stake but also impacts the brand image of the company. This study explores the sentiments and emotions surrounding layoffs through a comparative analysis using three natural language processing (NLP) tools: TextBlob, VADER, and the NRC Emotion Lexicon. A dataset of layoff-related tweets was collected over six months, pre-processed, and analyzed for sentiment polarity and emotional tone. The analysis revealed predominantly negative sentiments, with emotions like anger, sadness, and anticipation being prevalent. While TextBlob and VADER effectively gauged sentiment, VADER performed better in handling informal language, and the NRC Lexicon provided a more nuanced emotional profile. The study highlights the psychological toll of layoffs and the importance of employer transparency in mitigating anxiety. Future research should consider advanced NLP models like BERT for improved sentiment detection and track the evolution of layoff-related sentiments over time.