Sentiment Analysis on Job Descriptions in the Technology Sector: Measuring Positive and Negative Perceptions of Companies Using Natural Language Processing Techniques

Abstrak

Sentiment analysis in job descriptions plays a critical role in shaping employer branding and recruitment strategies. This study investigates the sentiment of job postings in the technology sector using NLP techniques, focusing on the emotional tone of descriptions across various job types, companies, and subcategories. The analysis reveals that positive sentiment predominates in job descriptions, with a clear trend towards using optimistic language to attract candidates. The findings show that Software Development positions tend to have the most positive tone, while roles such as IT Management exhibit a more balanced sentiment. Additionally, the use of inclusive language, such as "equal opportunity" and "years of experience", is prevalent in the descriptions, highlighting the growing importance of diversity and inclusivity in recruitment. Visualization tools like word clouds and trend analysis illustrate how sentiment shifts over time, with a noticeable increase in positive sentiment from 2020 onwards. The results underscore the potential of sentiment analysis and NLP in optimizing recruitment processes, aligning job descriptions with candidate expectations, and enhancing employer branding strategies.