Automated Social Media Sentiment Analysis through Novel User-generated Content on Major Social Media Platforms

This project aims to develop a machine learning model for sentiment analysis on social media platforms such as Twitter and YouTube and E-commerce websites like Amazon. Social media plays a pivotal role in shaping people’s views and ideas, making it essential to understand the sentiments expressed on these platforms. The primary challenge lies in the vast amounts of unstructured data, the ambiguity in human language, and the diverse range of opinions expressed. Manual analysis of such data is impractical, necessitating the use of machine learning techniques to extract meaningful insights. For businesses, it provides valuable consumer insights that can enhance marketing strategies. Policymakers can benefit by understanding public opinion on welfare schemes and identifying areas for improvement. Individuals can stay informed about emerging trends and issues. The project methodology encompasses several key steps. First, data is collected from diverse sources, including social media posts, comments, and reviews. The data is then preprocessed, involving cleaning, structuring, and labeling for machine learning purposes. Feature extraction transforms text data into numerical representations suitable for modeling. Multiple machine learning models are experimented with to select the one with the highest accuracy. Subsequently, the selected model is trained and evaluated. Finally, a user-friendly web interface is deployed, allowing users to input social media content for sentiment analysis. Deliverables include a trained machine learning model for sentiment analysis, a well-documented codebase, and a user-friendly web application for end-users. This project aims to provide a valuable tool for sentiment analysis on social media platforms, facilitating informed decision-making and enhancing our understanding of public sentiment in the digital age.