检索结果-南通市图书馆

Aspect-Based sentiment Analysis for polarity Estimation of Customer Reviews on Twitter

Computers, Materials & Continua 2021年第5期67卷 2203-2225页

作者： Ameen Banjar Zohair Ahmed Ali Daud Rabeeh Ayaz Abbasi Hussain Dawood Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddah21589Saudi Arabia Air University Islamabad44000Pakistan Department of Computer Science Islamabad44000Pakistan Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21589 Saudi Arabia

Most consumers read online reviews written by different users before making purchase decisions,where each opinion expresses some ***,sentiment analysis is currently a hot topic of *** particular,aspect-based sentiment analysis concerns the exploration of emotions,opinions and facts that are expressed by people,usually in the form of *** is crucial to consider polarity calculations and not simply categorize reviews as positive,negative,or ***,the available lexicon-based method accuracy is affected by limited *** of the available polarity estimation techniques are too general and may not reect the aspect/topic in question if reviews contain a wide range of information about different *** paper presents a model for the polarity estimation of customer reviews using aspect-based sentiment analysis(ABSA-PER).ABSA-PER has three major phases:data preprocessing,aspect co-occurrence calculation(CAC)and polarity estimation.A multi-domain sentiment dataset,Twitter dataset,and trust pilot forum dataset(developed by us by dened judgement rules)are used to verify *** outcomes show that ABSA-PER achieves better accuracy,i.e.,85.7%accuracy for aspect extraction and 86.5%accuracy in terms of polarity estimation,than that of the baseline methods.

关键词： Natural language processing sentiment analysis aspect co-occurrence calculation sentiment polarity customer reviews twitte

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

COVID-19 Public sentiment Insights: A Text Mining Approach to the Gulf Countries

引用

Computers, Materials & Continua 2021年第5期67卷 1613-1627页

作者： Saleh Albahli Ahmad Algsham Shamsulhaq Aeraj Muath Alsaeed Muath Alrashed Hafiz Tayyab Rauf Muhammad Arif Mazin Abed Mohammed Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science Univesity of GurjatPakistan School of Computer Science Guanzghou UniversityGuangzhou510006China College of Computer Science and Information technology University of Anbar11RamadiAnbarIraq

Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their *** COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media *** phenomenon caused a state of panic among *** studies were conducted to stop the spread of fake news to help people cope with the *** this paper,a semantic analysis of three levels(negative,neutral,and positive)is used to gauge the feelings of Gulf countries towards the pandemic and the lockdown,on basis of a Twitter dataset of 2 months,using Natural Language Processing(NLP)*** has been observed that there are no mixed emotions during the pandemic as it started with a neutral reaction,then positive sentiments,and lastly,peaks of negative *** results show that the feelings of the Gulf countries towards the pandemic depict approximately a 50.5%neutral,a 31.2%positive,and an 18.3%negative sentiment *** study can be useful for government authorities to learn the discrepancies between different populations from diverse areas to overcome the COVID-19 spread accordingly.

关键词： COVID-19 sentiment analysis natural language processing twitter social data mining sentiment polarity

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：