图书情报工作

图书情报工作 >

2015 , Vol. 59 >Issue 4: 100 - 105

DOI: https://doi.org/10.13266/j.issn.0252-3116.2015.04.015

情报研究

附加情感特征的在线问答社区信息质量自动化评价

  • 姜雯 ,
  • 许鑫 ,
  • 武高峰
展开
  • 1. 华东师范大学商学院信息学系 上海 200241;
    2. 北京林业大学信息学院 北京 100083
姜雯(ORCID:0000-0002-9619-2312),硕士研究生;武高峰(ORCID:0000-0002-8008-566X),硕士研究生。

收稿日期: 2014-12-22

  修回日期: 2015-02-05

  网络出版日期: 2015-02-20

收起

Online Q&A Community Automatically Information Quality Evaluation with Sentiment Feature

  • Jiang Wen ,
  • Xu Xin ,
  • Wu Gaofeng
Expand
  • 1. Department of InformationScience, Business School, East China Normal University, Shanghai 200241;
    2. College of Information, Beijing Forestry University, Beijing 100083

Received date: 2014-12-22

  Revised date: 2015-02-05

  Online published: 2015-02-20

Fold

摘要

[目的/意义] 考察在线问答社区信息中的情感特征以及其对在线问答社区信息质量自动化评价的影响。[方法/过程] 综合以往研究,提取Yahoo! Answers中的回答信息的文本特征、用户特征、时序特征等,并提出附加情感标注的回答特征,利用Weka机器学习的方法进行信息质量自动化分类预测。[结果/结论] 结果显示,在线问答社区信息中具有一定的情感特征且情感特征的加入能够提高分类预测的准确率。

关键词： 信息质量评价; 问答社区; 机器学习; 情感分析

本文引用格式

姜雯 , 许鑫 , 武高峰 . 附加情感特征的在线问答社区信息质量自动化评价[J]. 图书情报工作, 2015 , 59(4) : 100 -105 . DOI: 10.13266/j.issn.0252-3116.2015.04.015

Abstract

[Purpose/significance] To study the sentiment in online Q&A community and examine the effect of the sentimental features on online Q&A community information automatically quality evaluation.[Method/process] This article synthesized the previous researches, extracted the text features, user features, sequence features etc. and put forward the feature with sentiment tagging, using Weka machine learning to automatically evaluate the information quality.[Result/conclusion] The experiment result shows that the addition of sentiment feature can improve the classification prediction accuracy.

Key words： information quality evaluation; Q&A community; machine learning; sentiment analysis

参考文献

[1] Kim S, Oh J S, Oh S. Best-answer selection criteria in a social Q&A site from the user-oriented relevance perspective[J].Proceedings of the American Society for Information Science and Technology,2007,44(1):1-15.
[2] Kim S,Oh S.Users' relevance criteria for evaluating answers in a social Q&A site[J].Journal of the American Society for Information Science and Technology,2009,60(4):716-727.
[3] Zhu Zhemin,Bernhard D,Gurevych I.A multi-dimensional model for assessing the quality of answers in social Q&A[EB/OL].[2015-01-25].http://tuprints.ulb.tu-darmstadt.de/1940/1/TR_dimension_model.pdf.
[4] Shah C, Pomerantz J.Evaluating and predicting answer quality in community QA[C]//Proceedings of the 33rd International ACM SIGIR conference on Research and Development in Information Retrieval.New York: ACM,2010:411-418.
[5] Fichman P.A comparative assessment of answer quality on four question answering sites[J].Journal of Information Science, 2011,37(5):476-486.
[6] 贾佳,宋恩梅,苏环.社会化问答平台的答案质量评估——以"知乎"、"百度知道"为例[J].信息资源管理学报,2013(2):19-28.
[7] 孔维泽,刘奕群,张敏,等.问答社区中回答质量的评价方法研究[J].中文信息学报. 2011(1):3-8.
[8] 李晨,巢文涵,陈小明,等.中文社区问答中问题答案质量评价和预测[J].计算机科学. 2011,38(6):230-236
[9] 来社安,蔡中民.基于相似度的问答社区问答质量评价方法[J].计算机应用与软件. 2013,30(2):266-269.
[10] Jeon J, Croft W B, Lee J H, et al.A framework to predict the quality of answers with non-textual features[C]//Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:ACM, 2006: 228-235.
[11] Cai Yuanzhe, Chakravarthy S. Predicting answer quality in Q/A social networks: Using temporal features[R].Arlington:Department of Computer Science and Engineering, University of Texas at Arlington, 2011.
[12] 李涵. 中国社区及社区情感的历史追溯与实证研究[D].哈尔滨:哈尔滨工程大学,2006.
[13] Oh S, Worrall A, Yi Y J.Quality evaluation of health answers in yahoo! answers: A comparison between experts and users[J].Proceedings of the American Society for Information Science and Technology, 2011,48(1):1-3.
[14] Oh S,Yi Y J,Worrall A.Quality of health answers in social Q&A[J].Proceedings of the American Society for Information Science and Technology,2012,49(1):1-6.
[15] Ishikawa D,Kando N,Sakai T.What makes a good answer in community question answering? An analysis of assessors' criteria[EB/OL].[2015-01-25].http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings9/EVIA/03-EVIA2011-IshikawaD.pdf.
[16] 蒋楠,王鹏程.社会化问答服务中用户需求与信息内容的相关性评价研究——以"百度知道"为例[J].信息资源管理学报,2012(3):35-45.
[17] 杨立公,朱俭,汤世平. 文本情感分析综述[J]. 计算机应用,2013(6):1574-1578,1607.
[18] 魏慧玲. 文本情感分析在产品评论中的应用研究[D].北京:北京交通大学,2014.
[19] Wilson T,Hoffmann P,Somasundaran S,et al.OpinionFinder:A system for subjectivity analysis[C]//Proceedings of HLT/EMNLP on Interactive Demonstrations.Stroudsburg:Association for Computational Linguistics,2005:34-35.
[20] Gamon M,Aue A,Corston-oliver S,et al.Pulse:Mining customer opinions from free text[M].Berlin: Springer, 2005: 121-132.
[21] Shi Wei,Wang Hongwei,He Shaoyi.Sentiment analysis of Chinese micro-blogging based on sentiment ontology:A case study of'7.23 Wenzhou Train Collision'[J].Connection Science,2013,25(4):161-178.
[22] Chamlertwat W,Bhattarakosol P,Rungkasiri T,et al.Discovering consumer insight from Twitter via sentiment analysis[J].Journal of Universal Computer Science,2012,18(8):973-992.
[23] Hownet[EB/OL].[2014-08-20].http://www.keenage.com/.
[24] Weka 3: Data mining software in Java[EB/OL].[2014-08-20].http://www.cs.waikato.ac.nz/ml/weka/.
[25] 程克非,程蕾,黄永东. 基于J48决策树算法的水质评价方法[J]. 计算机工程,2012(11):264-267.
[26] Tian Qiongjie,Zhang Peng, Li Baoxin.Towards predicting the best answers in community-based question-answering services[EB/OL].[2015-01-25].http://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6096/6334.

Options
文章导航

/

〈 〉