Abstract: Sentiment analysis, a crucial yet complex task in natural language processing (NLP), is extensively employed to identify sentiment polarity within user-generated content. Traditional deep learning methods for textual sentiment analysis often overlook the influence of emotional modulation on extracting sentiment features. At the same time, their attention mechanisms primarily operate at the word or sentence levels. Such oversight of higher-level abstractions may hinder the learning of nuanced sentiment patterns, ultimately damaging the accuracy of sentiment analysis. Addressing these gaps, this study proposed a novel framework, the Two-State Enhanced LSTM (TS-ELSTM), which integrated Emotional Intelligence (EI) and a Feature Attention Mechanism (FAM) to enhance the identification of relevant features during selection. Furthermore, this study employed a dual-phase training strategy of LSTM to accelerate learning and minimize information loss. A dynamic topic-level attention mechanism is also introduced to optimize hidden text representation weights. By integrating EI with a topic-level attention mechanism, the proposed framework efficiently extracts valuable features and enhances the feature learning ability of the conventional LSTM model. This novelty attains emotion-aware learning through two key components: an emotion modulator and an emotion estimator, which successfully normalize the system’s learning dynamics by combining emotional context. The experimental outcomes demonstrated that the proposed approach achieved an accuracy of 84.20%, 94.12% using MR and IMDB, respectively. The proposed approach significantly improves sentiment analysis accuracy, outperforming traditional deep learning models by a notable margin.

Muhammad Naeem Aftab, Dost Muhammad Khan, Muhammad Zulqarnain and Muhammad Rizwan Akram, “An Enhanced LSTM Model Based on Feature Attention Mechanism and Emotional Intelligence for Advanced Sentiment Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160650

@article{Aftab2025,
title = {An Enhanced LSTM Model Based on Feature Attention Mechanism and Emotional Intelligence for Advanced Sentiment Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160650},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160650},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Muhammad Naeem Aftab and Dost Muhammad Khan and Muhammad Zulqarnain and Muhammad Rizwan Akram}
}