Authors - Jai Ramani, Tanay Kelkar, Darshil Shah, Divanshu Maheshwari, Archana Nanade Abstract - Anonymized employee reviews on platforms like Ambition-Box offer insights into workplace experiences such as salary, work culture, working hours, and management quality. However, manually analyzing large volumes of reviews is challenging and time-consuming. To overcome this limitation, an automated system is proposed to collect, process, and present employee sentiment in a structured and meaningful way. Using the technique of Aspect-Based Sentiment Analysis (ABSA), the system classifies reviews as positive or negative while identifying sentiment across key concerns such as salary, work-life balance, career growth, management quality, etc. To identify the keywords and their corresponding sentiment, this study utilizes the T5 model that is fine-tuned using the InstructABSA framework. Data is gathered through web scraping, ensuring coverage of employee opinions from multiple platforms. The resulting analysis highlights areas where companies excel or need improvement, providing actionable insights to enhance the workplace.
