Harnessing Social Media Sentiment Analysis for Wildlife Conservation

In today's digital age, social media platforms have become powerful tools for collecting public sentiment on various issues, including environmental conservation. This research employs data from Twitter, YouTube, TikTok, and Instagram to enhance the conservation efforts for endangered species through sentiment analysis. We collected and preprocessed a high-quality dataset from these platforms and applied multiple models to perform sentiment analysis. Among the models tested, Logistic Regression (LR) and Valence Aware Dictionary and sEntiment Reasoner (VADER) showed the highest accuracy rates. Key preprocessing steps included cleaning emojis, slang, and nonEnglish text to standardize the input data. Our results suggest that social media can be a strategic asset for conservationists by providing insights into public sentiment and engagement. Future work will focus on improving data processing techniques and exploring hybrid models to further boost the effectiveness of sentiment analysis in conservation efforts.