OPTIMIZING SENTIMENT INTERPRETATION OF COURSERA COURSE REVIEWS USING AN ADAPTIVE FISH SWARM OPTIMIZATION-INSPIRED RECURRENT NEURAL NETWORK (AFSO-RNN)

This study introduces an innovative approach, Adaptive Fish Swarm Optimization-Inspired Recurrent Neural Network (AFSO-RNN), to optimize sentiment interpretation of Coursera course reviews. The AFSO-RNN model combines the adaptive capabilities of fish swarm optimization with the power of recurrent neural networks. The recurrent neural network component processes the textual data of course reviews by capturing semantic meaning and context. It learns from sequential dependencies to extract relevant features for sentiment analysis. The adaptive fish swarm optimization component enhances the learning process of the recurrent neural network. Inspired by collective behaviour in fish swarms, it dynamically adjusts network parameters during training. The optimisation process explores and exploits optimal solutions by mimicking fish swarm movement and communication patterns, improving sentiment interpretation accuracy. Extensive experiments on a large dataset of Coursera course reviews demonstrate the superior performance of AFSO-RNN compared to traditional sentiment analysis techniques. The model’s optimized sentiment interpretation provides valuable insights into learner sentiments, enabling informed decision-making for instructors, administrators, and learners regarding course selection and improvement. This research contributes to sentiment analysis in online learning environments by showcasing the effectiveness of the AFSO-RNN model. By combining recurrent neural networks with adaptive fish swarm optimization, AFSO-RNN offers a promising avenue for enhancing the accuracy and efficiency of sentiment analysis for Coursera course reviews