NAACL 2013 Paper Mining User Relations from Online Discussions using Sentiment Analysis and PMF

中文简单介绍:本文对怎样基于情感分析和概率矩阵分解从网络论坛讨论中挖掘用户关系进行了深入研究。

论文出处:NAACL‘13.

英文摘要: Advances in sentiment analysis have enabled extraction of user relations implied in online textual exchanges such as forum posts. However,recent studies in this direction only consider direct relation extraction from text. As user interactions can be sparse in online discussions,we propose to apply collaborative filtering through probabilistic matrix factorization to generalize and improve the opinion matrices extracted from forum posts. Experiments with two tasks show that the learned latent factor representation can give good performance on a relation polarity prediction task and improve the performance of a subgroup detection task.

下载链接:http://aclweb.org/anthology/N/N13/N13-1041.pdf

开源Code链接:https://github.com/yangliuy/NLPForumPostOTE

Data链接:https://github.com/yangliuy/Debate-DataSets_NAACL13

原文地址:https://www.cnblogs.com/zhchoutai/p/8367059.html

时间: 2024-08-24 22:31:24

NAACL 2013 Paper Mining User Relations from Online Discussions using Sentiment Analysis and PMF的相关文章

Paper Weekly-Opinion mining and sentiment analysis

A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts http://www.aclweb.org/anthology/P04-1035 by B Pang -2004- ?Cited by 2242 Large-Scale Sentiment Analysis for News and Blogs http://icwsm.org/papers/3--G

CIKM 2013 Paper CQARank: Jointly Model Topics and Expertise in Community Question Answering

中文简介: 本文对如何在问答社区对用户主题兴趣及专业度建模分析进行了研究,并且提出了针对此问题的统计图模型Topics Expertise Model. 论文出处:CIKM'13. 英文摘要: Community Question Answering (CQA) websites, where people share expertise on open platforms, have become large repositories of valuable knowledge. To bri

ACL 2015 selected paper

ACL 2015 selected paper 概述(1) 开完 ACL 2015 大会,选了自己感兴趣的几十篇论文,大部分是自己已经读过的,做了一些概述.相信里面有很多错误,欢迎指正.另外,图文并茂版本在公众号查看,长微博复制图片也许有很多错误显示不出来. 1. Text to 3D Scene Generation with Rich Lexical Grounding Angel Chang, Will Monroe, Manolis Savva, Christopher Potts, C

机器学习和深度学习资料合集

机器学习和深度学习资料合集 注:机器学习资料篇目一共500条,篇目二开始更新 希望转载的朋友,你可以不用联系我.但是一定要保留原文链接,因为这个项目还在继续也在不定期更新.希望看到文章的朋友能够学到更多.此外:某些资料在中国访问需要梯子. <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in

[转]机器学习和深度学习资料汇总【01】

本文转自:http://blog.csdn.net/sinat_34707539/article/details/52105681 <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen

关于机器学习和深度学习的资料

声明:转来的,原文出处:http://blog.csdn.net/achaoluo007/article/details/43564321 编者按:本文收集了百来篇关于机器学习和深度学习的资料,含各种文档,视频,源码等.而且原文也会不定期的更新,望看到文章的朋友能够学到更多. <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost 到随机森林.Deep Learning. &

awesome-nlp

awesome-nlp  A curated list of resources dedicated to Natural Language Processing Maintainers - Keon Kim, Martin Park Please read the contribution guidelines before contributing. Please feel free to pull requests, or email Martin Park ([email protect

机器学习&amp;深度学习资料分享

感谢:https://github.com/ty4z2008/Qix/blob/master/dl.md <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost 到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室 Jurgen Schmidhuber

近200篇机器学习&amp;amp;深度学习资料分享

编者按:本文收集了百来篇关于机器学习和深度学习的资料,含各种文档,视频,源码等.并且原文也会不定期的更新.望看到文章的朋友能够学到很多其它. <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍非常全面,从感知机.神经网络.决策树.SVM.Adaboost 到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室