Sentiment analysis is a powerful tool for transforming consumer feedback into actionable insights, enabling businesses to refine strategies and improve customer experiences. This study evaluates the performance of machine learning models, including Logistic Regression, Random Forest, SVM, LSTM, and BERT, for sentiment classification on a diverse dataset of customer reviews. BERT outperformed other models, achieving an AUC-ROC of 0.97 and an accuracy of 94.2%, showcasing its ability to capture complex semantic patterns in text. The findings provide businesses with critical insights into consumer sentiment, guiding decision-making and enhancing competitive advantage. The study also addresses challenges such as data ambiguity, ethical considerations, and computational demands, offering practical recommendations for implementing scalable and effective sentiment analysis solutions. These results demonstrate the potential of machine learning-driven sentiment analysis in shaping customer-focused business strategies and fostering growth in a data-driven market.

Seaweed has emerged as a promising resource in the quest for renewable energy. This study explores the potential of residual carrageenan extract from Eucheuma cottonii as a sustainable feedstock for bioethanol production. Carrageenan, a polysaccharide widely used in food and industrial applications, leaves significant residues after extraction. These residues are rich in fermentable sugars, making them an ideal candidate for bioethanol synthesis. The research investigates the optimal conditions for hydrolysis and fermentation processes to maximize ethanol yield. Results demonstrate that carrageenan residues can produce bioethanol efficiently, presenting an innovative solution for utilizing waste from the seaweed industry. This approach contributes to sustainable energy production while addressing environmental concerns related to seaweed waste.