P. C. D. Kalaivaani, K. Sathyarajasekaran, N. Krishnamoorthy, T. Kumaravel
{"title":"利用产品评论进行情感分析的混合 HAN-CNN 与方面词提取技术","authors":"P. C. D. Kalaivaani, K. Sathyarajasekaran, N. Krishnamoorthy, T. Kumaravel","doi":"10.1111/coin.12698","DOIUrl":null,"url":null,"abstract":"<p>In this article, an intensive sentiment analysis approach termed Hierarchical attention-convolutional neural network (HAN-CNN) has been proposed using product reviews. Firstly, the input product review is subjected to Bidirectional Encoder Representation from Transformers (BERT) tokenization, where the input data of each sentence are partitioned into little bits of words. Thereafter, Aspect Term Extraction (ATE) is carried out and feature extraction is completed utilizing some features. Finally, sentiment analysis is accomplished by the developed HAN-CNN, which is formed by combining a Hierarchical Attention Network (HAN) and a Convolutional Neural Network (CNN). Moreover, the proposed HAN-CNN achieved a greater performance with maximum accuracy, recall and F1-Score of 91.70%, 90.60% and 91.20%, respectively.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"40 5","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid HAN-CNN with aspect term extraction for sentiment analysis using product review\",\"authors\":\"P. C. D. Kalaivaani, K. Sathyarajasekaran, N. Krishnamoorthy, T. Kumaravel\",\"doi\":\"10.1111/coin.12698\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In this article, an intensive sentiment analysis approach termed Hierarchical attention-convolutional neural network (HAN-CNN) has been proposed using product reviews. Firstly, the input product review is subjected to Bidirectional Encoder Representation from Transformers (BERT) tokenization, where the input data of each sentence are partitioned into little bits of words. Thereafter, Aspect Term Extraction (ATE) is carried out and feature extraction is completed utilizing some features. Finally, sentiment analysis is accomplished by the developed HAN-CNN, which is formed by combining a Hierarchical Attention Network (HAN) and a Convolutional Neural Network (CNN). Moreover, the proposed HAN-CNN achieved a greater performance with maximum accuracy, recall and F1-Score of 91.70%, 90.60% and 91.20%, respectively.</p>\",\"PeriodicalId\":55228,\"journal\":{\"name\":\"Computational Intelligence\",\"volume\":\"40 5\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/coin.12698\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/coin.12698","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Hybrid HAN-CNN with aspect term extraction for sentiment analysis using product review
In this article, an intensive sentiment analysis approach termed Hierarchical attention-convolutional neural network (HAN-CNN) has been proposed using product reviews. Firstly, the input product review is subjected to Bidirectional Encoder Representation from Transformers (BERT) tokenization, where the input data of each sentence are partitioned into little bits of words. Thereafter, Aspect Term Extraction (ATE) is carried out and feature extraction is completed utilizing some features. Finally, sentiment analysis is accomplished by the developed HAN-CNN, which is formed by combining a Hierarchical Attention Network (HAN) and a Convolutional Neural Network (CNN). Moreover, the proposed HAN-CNN achieved a greater performance with maximum accuracy, recall and F1-Score of 91.70%, 90.60% and 91.20%, respectively.
期刊介绍:
This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.