Automated sentimental analysis using heuristic-based CNN-BiLSTM for E-commerce dataset

Abstract

The sentimental analysis is processed according to structural detection, extraction, quantification, and evaluation of impacts and information present in natural language processing, biometric data, computer-language society, and text analysis. Advanced implementations in data analytics occur, which allows for identifying the underlining trends based on certain efficient computational models. Some of the social and e-commerce platforms consist of numerous reviews regarding the online products that are posted by the end-users. This helps the developers to understand priceless insight during the process of product design. The most significant aim of this work is to develop a Heuristic-based sentimental analysis for E-Commerce data. Here, the pre-processing of input E-Commerce data is performed by blank space removal, stop word removal, and stemming. Here, the statistical features are extracted using Cross Similarity Score (CSS) and Joint Similarity Score (JSS) regarding “positive, negative, and neutral keywords”. In addition, the word2vec features are also extracted. From the two sets of features, “Convolutional Neural Network (CNN) with Bidirectional Long Short Term Memory (BiLSTM)” is separately used that is named as Optimal Hybrid CNN-BiLSTM (OH-CNN-BiLSTM), and performed the hybridization of two models for the sentimental analysis. The designed CNN-BiLSTM technique is ensured better outcomes when processing long text as it provides advantages through the ability of CNN to extract the features and superiority of Bi-LSTM in learning the bidirectional dependencies of the long-term text. The main novelty of this work is to develop an improved Galactic Swarm Optimization (IGSO) algorithm for improving the hybridized model, as it provides faster convergence when getting the accurate solution on a wide range of dimensional space and is also able to solve the optimization issues with multimodal benchmark data. The evaluation of the suggested algorithm is done with various E-Commerce data and secures high accuracy on sentimental analysis and prediction while comparing with the traditional models.