Sentiment analysis plays a crucial role across various domains, requiring advanced methods for effective dimensionality reduc- tion and feature extraction. This study introduces a novel framework, multi-objective manifold representation (MOMR) for opin- ion mining, which uniquely integrates deep global features with local manifold representations to capture comprehensive data patterns efficiently. Unlike existing methods, MOMR employs advanced dimensionality reduction techniques combined with a self-attention mechanism, enabling the model to focus on contextually relevant textual elements. This dual approach not only enhances interpretability but also improves the performance of sentiment analysis. The proposed method was rigorously evalu- ated against both classical techniques such as long short-term memory (LSTM), naive Bayes (NB) and support vector machines (SVMs), and modern state-of-the-art models including recurrent neural networks (RNN) and convolutional neural networks (CNN). Experiments on diverse datasets: IMDB, Fake News, Twitter and Yelp demonstrated the superior accuracy and robust- ness of MOMR. By outperforming competing methods in terms of generalizability and effectiveness, MOMR establishes itself as a significant advancement in sentiment analysis, with broad applicability in real-world opinion mining tasks (https://github. com/pshtirahman/Sentiment-Analysis.git).
