Deep Learning Approaches for Accurate Sentiment Analysis of Online Consumer Feedback

Swathi Ganesan, N. Somasiri, Chandima Colombage
{"title":"Deep Learning Approaches for Accurate Sentiment Analysis of Online Consumer Feedback","authors":"Swathi Ganesan, N. Somasiri, Chandima Colombage","doi":"10.1109/ICCCI56745.2023.10128231","DOIUrl":null,"url":null,"abstract":"Over the earlier time, a category of machine learning, called deep learning, has attained significant achievements in several computer vision tasks such as image classification, object detection, semantic segmentation, pattern recognition and image classification generation. Deep learning objectives at finding various levels of dispersed representations, which have been proven to be discriminatively effective in many tasks. Distributed statement depicts similar information highlights across different adaptable and reliant layers. Each layer characterizes the data with a similar degree of exactness, however adapted to the degree of scale. The implementation of deep learning techniques depends greatly on the variety of data interpretation (or features) on which they are used. Artificial intelligence plans to understand interpretations of information regularly by changing over it or isolating components as of it, which creates it simpler to play out an undertaking like order or extrapolation.","PeriodicalId":205683,"journal":{"name":"2023 International Conference on Computer Communication and Informatics (ICCCI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Computer Communication and Informatics (ICCCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCI56745.2023.10128231","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Over the earlier time, a category of machine learning, called deep learning, has attained significant achievements in several computer vision tasks such as image classification, object detection, semantic segmentation, pattern recognition and image classification generation. Deep learning objectives at finding various levels of dispersed representations, which have been proven to be discriminatively effective in many tasks. Distributed statement depicts similar information highlights across different adaptable and reliant layers. Each layer characterizes the data with a similar degree of exactness, however adapted to the degree of scale. The implementation of deep learning techniques depends greatly on the variety of data interpretation (or features) on which they are used. Artificial intelligence plans to understand interpretations of information regularly by changing over it or isolating components as of it, which creates it simpler to play out an undertaking like order or extrapolation.
基于深度学习的在线消费者反馈情感分析方法
在较早的时间里,机器学习的一个类别,称为深度学习,在图像分类、目标检测、语义分割、模式识别和图像分类生成等几个计算机视觉任务中取得了重大成就。深度学习的目标是找到不同层次的分散表征,这在许多任务中被证明是判别有效的。分布式语句在不同的适应性层和依赖层之间描述相似的信息亮点。每一层都以相似的精确程度来描述数据的特征,但要适应规模的程度。深度学习技术的实现在很大程度上取决于所使用的数据解释(或特征)的多样性。人工智能计划通过改变信息或隔离信息的组成部分来定期理解对信息的解释,这使得执行排序或外推等任务变得更容易。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信