An optimization enabled Hierarchical Attention -Deep LSTM model for sentiment analysis on cloth products from customer rating

Abstract

The primary aim of the study endeavour is to introduce a deep learning approaches augmented with optimization techniques to conduct sentiment analysis on apparel products, utilize customer reviews and ratings as foundational data. Consequently, a review of a clothing item is utilized as input, which undergoes pre-processing involving the elimination of stop words and stemming to eradicate superfluous information. In parallel, critical features are extracted from the pre-processed data to facilitate effective categorization. Thereafter, feature extraction is executed through execution of Term frequency-inverse document frequency (TF-IDF), SentiWordNet features, positive sentiment scores, negative sentiment scores, the count of capitalized words, and hashtags. Subsequently, feature fusion is conducted utilizing the proposed Trend factor smoothing-Siberian Tiger Optimization (TS-STO), which is innovatively premeditated by integrating trend factor smoothing within the update process of Siberian Tiger Optimization (STO). Ultimately, sentiment analysis is conducted through the implementation of HA-Deep LSTM, which is conceived by merging Hierarchical Attention Network with Deep LSTM. Experimental analysis portrayed that presented approach conquered an accuracy of 95.9 %, a sensitivity of 96.1 % and specificity of 94.2 %.