Rider Driven African Vulture Optimization with Multi Kernel Structured Text Convolutional Neural Network for Classifying e-Commerce Reviews

Opinion mining is a natural language processing based on sentiment classification technique to determine the sentiment of the reviews. The major existing text Convolutional Neural Network (CNN) algorithms are derived based on 3×3 size kernels which extract ineffective review text-features and lead to less classification accuracy. Moreover, most of the traditional CNN versions output three classes such as positive, negative, and neutral as their classification results. Hence, a novel algorithm namely ‘RAVO driven Multi-Size Kernel structured Text CNN for classifying ecommerce reviews (MSK-TCNN-RAVO)’ is proposed in this work. This proposed approach utilizes five multi-size kernels (3×7,5×7,1×3,1×5,1×7), multi-dimensional kernels (1D & 2D), and integrates varying size kernels to extract text-features effectively. In addition, the performance of multi-kernel CNN is highly enhanced by RAVO algorithm based on rider optimization. Moreover, the proposed approach is highly effective to process 'review-stop-words removal' that decrease the complexity and time consumption of the opinion mining process. Most existing systems use single pooling operations which reduce feature map processing performance, hence, dual pooling operations (both Max and Average pooling) are employed in this research. Furthermore, it is configured to generate five classification outputs such as bad, fair, neutral, good, and excellent to support better decision-making with 95.5% accuracy. This method is evaluated using different quality metrics and five review-databases to measure the performance, and the results reveal that the proposed method outperforms the other existing review classification algorithms.