{"title":"基于递归神经网络(RNN)的亚马逊产品评论情感分析比较研究","authors":"","doi":"10.30534/ijatcse/2022/111132022","DOIUrl":null,"url":null,"abstract":"The problem of sentiment analysis on Amazon products is addressed in this research. In reality, because opinions are at the center of practically all human activity, sentiment analysis tools are used in almost every economic and social arena. They are also major influencers of our actions. The recurrent neural network (RNN) model is used to classify the product reviews of Amazon in this paper. Furthermore, using this family of models, which is particularly well-suited to the processing of sequential data, we were able to construct comprehensible text from an initial sequence on a character- by-character basis. As a result, we used three Amazon review datasets to estimate the authors' attitudes. As a result, we achieve results of 85% accuracy, and which are comparable to the greatest state-of-the-art models in this area.","PeriodicalId":129636,"journal":{"name":"International Journal of Advanced Trends in Computer Science and Engineering","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparative study of Sentiment Analysis on Amazon Product Reviews using Recurrent Neural Network (RNN)\",\"authors\":\"\",\"doi\":\"10.30534/ijatcse/2022/111132022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of sentiment analysis on Amazon products is addressed in this research. In reality, because opinions are at the center of practically all human activity, sentiment analysis tools are used in almost every economic and social arena. They are also major influencers of our actions. The recurrent neural network (RNN) model is used to classify the product reviews of Amazon in this paper. Furthermore, using this family of models, which is particularly well-suited to the processing of sequential data, we were able to construct comprehensible text from an initial sequence on a character- by-character basis. As a result, we used three Amazon review datasets to estimate the authors' attitudes. As a result, we achieve results of 85% accuracy, and which are comparable to the greatest state-of-the-art models in this area.\",\"PeriodicalId\":129636,\"journal\":{\"name\":\"International Journal of Advanced Trends in Computer Science and Engineering\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Advanced Trends in Computer Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.30534/ijatcse/2022/111132022\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Advanced Trends in Computer Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30534/ijatcse/2022/111132022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparative study of Sentiment Analysis on Amazon Product Reviews using Recurrent Neural Network (RNN)
The problem of sentiment analysis on Amazon products is addressed in this research. In reality, because opinions are at the center of practically all human activity, sentiment analysis tools are used in almost every economic and social arena. They are also major influencers of our actions. The recurrent neural network (RNN) model is used to classify the product reviews of Amazon in this paper. Furthermore, using this family of models, which is particularly well-suited to the processing of sequential data, we were able to construct comprehensible text from an initial sequence on a character- by-character basis. As a result, we used three Amazon review datasets to estimate the authors' attitudes. As a result, we achieve results of 85% accuracy, and which are comparable to the greatest state-of-the-art models in this area.