Identifying the Influences Behind the LinkedIn Posts using Topic Modeling and Sentiment Analysis

The proliferation of social networking in the modern world has made it a ubiquitous presence in people’s lives. On a daily basis, individuals post and share their opinions, rating products, and engage in business-related activities. Social media platforms play a crucial role in the business field, facilitating the establishment of relationships with prospects and clients. As such, LinkedIn has emerged as the ideal online platform for building trust by sharing success stories, business promotions, and recommendations through posts. This professional website serves as a means of connecting the world’s professionals with the general public. At the same time, many people post opinions and professional content regularly, and only a select few post become influenced by reaching larger community. The factors responsible for this influence remain unknown, prompting us to propose a solution based on topic modeling and sentiment analysis. Our project aims to identify the influences behind LinkedIn posts by examining the role of media content in posts, and by utilizing natural language processing techniques to analyze the underlying aspects of posts which then examined using topic modeling and sentiment analysis to identify subtopics and aspects present in the posts.