Atlas L Godlin, Arockiam Daniel, Muthusamy Arvindhan, Balusamy Balamurugan, Selvarajan Shitharth, Al-Shehari Taher, Alsadhan Nasser A
Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, India.
ASET-CSE, Amity University, Gwalior, Madhya Pradesh, India.
Sci Rep. 2025 May 13;15(1):16642. doi: 10.1038/s41598-025-01104-0.
With the advent of Web 2.0 and popularization of online shopping applications, there has been a huge upsurge of user generated content in recent times. Leading companies and top brands are trying to exploit this data and analyze the market demands and reach of their products among consumers using opinion mining. Sentiment analysis is a hot topic of research in the e-commerce industry. This paper proposes such a novel sentence level sentiment analysis approach for mining online product reviews using natural language processing and deep learning techniques. The proposed model consists of various stages like web crawling and collecting product reviews, preprocessing, feature extraction, sentiment analysis and polarity classification. The input reviews are preprocessed using natural language processing techniques like tokenization, lemmatization, stop word removal, named entity recognition and part of speech tagging. Feature extraction is done using bidirectional gated recurrent unit shortly called as BiGRU feature extractor and the sentiments are classified into three polarities such as positive, negative and neutral using a hybrid recurrent neural network based long short-term memory classifier. The specific combination of techniques employed here and applying it to a new kind of online product review is making the proposed model to be novel. Performance evaluation metrics such as accuracy, precision, recall, F measure and AUC are calculated for the proposed model and compared with many existing techniques like deep convolutional neural network, multilayer perceptron, CapsuleNet and generative adversarial networks. The proposed model can be used in a variety of applications like market research, social network mining, recommendation systems, brand analysis, product quality management etc. and is found to generate promising results when compared to prevailing models.
随着Web 2.0的出现以及在线购物应用的普及，近年来用户生成内容急剧增加。领先企业和顶级品牌正试图利用这些数据，并通过观点挖掘来分析市场需求以及其产品在消费者中的影响力。情感分析是电子商务行业研究的热门话题。本文提出了一种新颖的句子级情感分析方法，用于使用自然语言处理和深度学习技术挖掘在线产品评论。所提出的模型包括网络爬虫和收集产品评论、预处理、特征提取、情感分析和极性分类等多个阶段。使用诸如词法分析、词形归约、停用词去除、命名实体识别和词性标注等自然语言处理技术对输入评论进行预处理。使用双向门控循环单元（简称为BiGRU特征提取器）进行特征提取，并使用基于混合递归神经网络的长短期记忆分类器将情感分为积极、消极和中性三种极性。这里采用的技术的特定组合并将其应用于一种新型的在线产品评论，使得所提出的模型具有新颖性。为所提出的模型计算了诸如准确率、精确率、召回率、F值和AUC等性能评估指标，并与深度卷积神经网络、多层感知器、胶囊网络和生成对抗网络等许多现有技术进行了比较。所提出的模型可用于市场研究、社交网络挖掘、推荐系统、品牌分析、产品质量管理等多种应用，并且与现有模型相比，发现能产生有前景的结果。
