SOCIAL MEDIA SENTIMENT ANALYSIS FOR COLLEGE BRANDING

Abstract: Social media serves as a vital platform for individuals and organizations to share experiences and opinions. Colleges utilize social media reviews to engage with students, alumni, and prospective applicants while shaping their public perception. This study proposes a sentiment analysis system employing advanced Machine Learning (ML) techniques to analyze college-related social media reviews. Using state-of-the-art models such as BERT (Bidirectional Encoder Representations from Transformers) and LSTMs (Long Short-Term Memory), this approach enhances sentiment classification, including gender-based sentiment analysis. The system provides educational institutions with actionable insights to refine branding strategies, improve student engagement, and strengthen institutional reputation.

Keywords: Social Media, College Branding, Sentiment Analysis, Machine Learning, NLP, BERT, LSTM, Emotion Categorization.