Predicting best answer in community questions based on content and sentiment analysis

Dalia Elalfy, Walaa K. Gad, R. Ismail
{"title":"Predicting best answer in community questions based on content and sentiment analysis","authors":"Dalia Elalfy, Walaa K. Gad, R. Ismail","doi":"10.1109/INTELCIS.2015.7397282","DOIUrl":null,"url":null,"abstract":"Community question answering sites are gained much popularity in the last few years because of the wide spread of the internet and the facilities that these sites offer in question asking and answering processes. Community question answering sites are here to save the asker's time and effort and make him/her ask in a natural language and get the answer also back in natural language and from experts. To achieve these goals there are many challenges. Some of these challenges are for example, many questions appear to non-experts so we need to direct the questions to experts in the question category and specifying the best answer to a given question and etc. In this paper, we propose a novel model to find the best answer by using features that are based on question and answer content, answer context and the relation between question and its answers. We conducted experiments to train classifiers using our new added features and the accuracy of the best answer prediction result was very promising.","PeriodicalId":6478,"journal":{"name":"2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INTELCIS.2015.7397282","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

Community question answering sites are gained much popularity in the last few years because of the wide spread of the internet and the facilities that these sites offer in question asking and answering processes. Community question answering sites are here to save the asker's time and effort and make him/her ask in a natural language and get the answer also back in natural language and from experts. To achieve these goals there are many challenges. Some of these challenges are for example, many questions appear to non-experts so we need to direct the questions to experts in the question category and specifying the best answer to a given question and etc. In this paper, we propose a novel model to find the best answer by using features that are based on question and answer content, answer context and the relation between question and its answers. We conducted experiments to train classifiers using our new added features and the accuracy of the best answer prediction result was very promising.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于内容和情感分析预测社区问题的最佳答案
由于互联网的广泛普及以及这些网站在提问和回答过程中提供的设施,社区问答网站在过去几年中变得非常受欢迎。社区问答网站在这里节省了提问者的时间和精力,让他/她用自然语言提问,并得到自然语言和专家的答案。要实现这些目标,有许多挑战。其中一些挑战是,例如,许多问题似乎是非专家,所以我们需要将问题直接交给问题类别中的专家,并指定给定问题的最佳答案等等。在本文中,我们提出了一个新的模型,利用基于问题和答案内容、答案上下文以及问题和答案之间的关系的特征来寻找最佳答案。我们使用新添加的特征进行了训练分类器的实验,最佳答案预测结果的准确性非常有希望。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
On the use of probabilistic model-checking for the verification of prognostics applications Prospective, knowledge based clinical risk analysis: The OPT-model Partial deduction in predicate calculus as a tool for artificial intelligence problem complexity decreasing XML summarization: A survey Finding the pin in the haystack: A Bot Traceback service for public clouds
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1