Hamiltonian deep neural network fostered sentiment analysis approach on product reviews

AbstractIn recent times, online shopping has become commonly used method for consumers to make purchases and engage in consumption with the rapid advancement of Internet technology. Enhancing user satisfaction is achievable through sentiment analysis (SA) of the enormous user evaluations found on e-commerce platforms. However, accurately predicting the sentiment orientations of these user reviews remains a challenge due to varying sequence lengths, text arrangements, and intricate logic. Nowadays, sentiment analysis is widely employed to assess customer feedback, which holds great significance in determining a product's success. In the past, people relied on word-of-mouth reviews to judge a product's quality. This practice of sentiment analysis is extensively applied in social media. Natural language processing (NLP) plays a crucial role in deciphering sentiment, also referred to as opinion mining or emotion AI, as it encompasses the collective perception of customers. In this manuscript, a Hamiltonian Deep Neural Networks-based Sentiment Analysis on Product Recommendation System (HDNN-SCOA-SA-PR) is proposed. First, the data are gathered from Amazon Product Reviews dataset. Then the data are pre-processed utilizing adaptive self-guided filtering for space tokenization, Gensim lemmatization, and Snowball stemming. By using Structured Optimal Graph-Based Sparse Feature Extraction, the features are extracted. Extracted features are selected using Single Candidate Optimization A...
Source: European Journal of Applied Physiology - Category: Physiology Source Type: research