Sophisticated Kalman Filtering-Based Neural Network for Analyzing Sentiments in Online Courses

In the era of online education, understanding and analyzing the sentiments expressed by students in online courses is crucial for improving the learning experience and course effectiveness. This paper addresses the problem of sentiment analysis in online courses and proposes a novel approach called Sophisticated Kalman Filtering-Based Neural Network (SKFNN) for accurate sentiment analysis. The proposed SKFNN algorithm combines the power of Kalman filtering and neural networks to capture the complex sentiment patterns in online course reviews. It leverages sophisticated learning to extract meaningful features from the data and employs Kalman filtering techniques to reduce noise and enhance sentiment analysis accuracy. To evaluate the performance of SKFNN, a comprehensive dataset of online course reviews is used. The dataset contains many student reviews, including their ratings and textual feedback. The classification accuracy (CA) and F-measure (FM) metrics are employed to measure the effectiveness of SKFNN in sentiment analysis. The experimental results demonstrate the superior performance of SKFNN compared to existing approaches. With an impressive CA of 93.364% and an FM of 93.511%, SKFNN outperforms current state-of-the-art-algorithms. These results highlight the capability of SKFNN to accurately classify sentiments expressed in online course reviews and provide valuable insights for course improvements.