基于标签的微博关键词抽取排序方法

doi:10.11772/j.issn.1001-9081.2016.02.0563

计算机应用 ›› 2016, Vol. 36 ›› Issue (2): 563-567.DOI: 10.11772/j.issn.1001-9081.2016.02.0563

基于标签的微博关键词抽取排序方法

叶菁菁, 李琳, 钟珞

武汉理工大学计算机科学与技术学院, 武汉 430070

收稿日期:2015-09-02 修回日期:2015-10-24 发布日期:2016-02-03 出版日期:2016-02-10
通讯作者: 李琳(1977-),女,湖南衡阳人,副教授,博士,CCF会员,主要研究方向:社会计算、信息检索及推荐系统。
作者简介:叶菁菁(1992-),女,江苏盐城人,硕士研究生,主要研究方向:自然语言处理、大数据分析;钟珞(1957-),男,湖北武汉人,教授,博士,CCF会员,主要研究方向:智能方法、软件工程。
基金资助:
国家社会科学基金资助项目(15BGL048);国家863计划项目(2015BAA072)。

Keyword extraction method for microblog based on hashtag

YE Jingjing, LI Lin, ZHONG Luo

School of Computer Science and Technology, Wuhan University of Technology, Wuhan Hubei 430070, China

Received:2015-09-02 Revised:2015-10-24 Online:2016-02-03 Published:2016-02-10

摘要/Abstract

摘要： 针对微博关键词抽取准确率不高的问题,提出一种基于标签优先的抽取排序方法。该方法利用微博本身具有的社交特征——标签,从微博内容集中抽取关键词。该方法首先根据微博自身建立初始词与微博之间的加权图,再将基于标签的随机游走方法应用于图中,随机游走反复跳跃到标签词节点上,经过一系列迭代得出每个词的平稳概率,并通过概率决定词的最终排序。该抽取方法根据真实的新浪微博内容进行测验,结果显示,与通过词与词的加权图来抽取关键词相比,基于标签的微博关键词抽取方法准确率提高了50%,在实际应用中能够有效提高关键词抽取的正确率。

关键词: 关键词抽取, 微博, 标签, 随机游走, 加权策略

Abstract: A hashtag based method was proposed to solve the problem how to accurately extract keywords from microblog. Hashtag, the social feature of a microblog was used to extract keywords from microblog content. A word-post weighted graph was built firstly, then a random walker was used on the graph by jumping to any hashtag node repeatedly. At last, every word rank was determined by its probability which would not change after walker iteration. The experiments were conducted on real microblogs from Sina platform. The results show that, compared to word-word graph method, the proposed hashtag-based approach gets higher accuracy of keyword extraction by 50%.

Key words: keyword extraction, microblog, hashtag, random walk, weighting strategy

中图分类号:

TP181

叶菁菁, 李琳, 钟珞. 基于标签的微博关键词抽取排序方法[J]. 计算机应用, 2016, 36(2): 563-567.

YE Jingjing, LI Lin, ZHONG Luo. Keyword extraction method for microblog based on hashtag[J]. Journal of Computer Applications, 2016, 36(2): 563-567.

参考文献

[1] KWAK H, LEE C, PARK H, et al. What is Twitter, a social network or a news media?[C]//WWW '10: Proceedings of the 19th International Conference on World Wide Web. New York: ACM, 2010: 591-600.
[2] WU W, ZHANG B, OSTENDORF M. Automatic generation of personalized annotation tags for Twitter users[C]//HLT '10: Human Language Technologies: Proceedings of the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2010: 689-692.
[3] ZHAO W X, JIANG J, HE J, et al. Topical keyphrase extraction from Twitter[C]//HLT '11: Human Language Technologies: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 2011: 379-388.
[4] WANG W, XU H, YANG W, et al. Constrained-hLDA for topic discovery in Chinese microblogs[C]//PAKDD '14: Proceedings of the 18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, LNCS 8444. Berlin: Springer, 2014: 608-619.
[5] HU X, TANG J L, LIU H. Leveraging knowledge across media for spammer detection in microblogging[C]//SIGIR '14: Proceedings of the 37th Annual ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2014: 547-556.
[6] VOSECKY J, LEUNG K W-T, NG W. Collaborative personalized Twitter search with topic-language models[C]//SIGIR '14: Proceedings of the 37th Annual ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2014: 53-62.
[7] LIU Z Y, CHEN X X, SUN M S. Mining the interests of Chinese microbloggers via keyword extraction[J]. Frontiers of Computer Science, 2012, 6(1): 76-87.
[8] HAVELIWALA T H. Topic-sensitive PageRank: a context-sensitive ranking algorithm for Web search[J]. IEEE Transactions on Knowledge and Data Engineering, 2003, 15(4): 784-796.
[9] GRINEVA M, GRINEV M, LIZORKIN D. Extracting key terms from noisy and multitheme documents[C]//WWW '09: Proceedings of the 18th International Conference on World Wide Web. New York: ACM, 2009: 661-670.
[10] MIHALCEA R, TARAU P. TextRank: bringing order into texts[C]//EMNLP '04: Proceedings of the 2004 Conference on Empirical Methods on Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2004: 404-411.
[11] 李鹏,王斌,石志伟,等.Tag-TextRank:一种基于Tag的网页关键词抽取方法[J].计算机研究与发展,2012,49(11):2344-2351 (LI P, WANG B, SHI Z W, et al. Tag-TextRank: a Webpage keyword extraction method based on tags[J]. Journal of Computer Research and Development, 2012, 49(11): 2344-2351.)
[12] LIU Z, HUANG W, ZHENG Y, et al. Automatic keyphrase extraction via topic decomposition[C]//EMNLP '10: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA: Association for Computational Linguistics, 2010: 366-376.
[13] WANG J, LIU J,WANG C. Keyword extraction based on PageRank[C]//PAKDD '07: Proceedings of the 11th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, LNCS 4426. Berlin: Springer-Verlag, 2007: 857-864.
[14] WANG X, WANG L, LI J W, et al. Exploring simultaneous keyword and key sentence extraction: Improve graph-based ranking using Wikipedia[C]//CIKM '12: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. New York: ACM, 2012: 2619-2622.
[15] WAN X J, XIAO J G. Single document keyphrase extraction using neighborhood knowledge[C]//AAAI '08: Proceedings of the 23rd National Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press, 2008: 855-860.
[16] LI Z H, ZHOU D, JUAN Y-F, et al. Keyword extraction for social snippets[C]//WWW '10: Proceeding of the 19th International Conference on World Wide Web. New York: ACM, 2010: 1143-1144.
[17] TURNEY P D. Learning algorithms for keyphrase extraction[J]. Information Retrieval, 2000, 2(4): 303-336.
[18] ZHANG W, FENG W, WANG J Y. Integrating semantic relatedness and words' intrinsic features for keyword extraction[C]//IJCAI '13: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press, 2013: 2225-2231.
[19] TONG H H, FALOUTSOS C, PAN J-Y. Fast random walk with restart and its applications[C]//ICDM '06: Proceedings of the Sixth International Conference on Data Mining. Washington, DC: IEEE Computer Society, 2006: 613-622.
[20] MANNING C D, RAGHAVAN P, SCHUTZE H. Introduction to information retrieval[M]. Cambridge, UK: Cambridge University Press, 2008: 116-122.
[21] PAGE L, BRIN S, MOTWANI R, et al. The PageRank citation ranking: bringing order to the Web: SIDL-WP-1999-0120 [R]. Stanford Digital Library Technologies Project, 1998: 98.
[22] ZHOU D Y, BOUSQUET O, LAL T N, et al. Learning with local and global consistency[C]//NIPS 2003: Advances in Neural Information Processing Systems 16. Cambridge, MA: MIT Press, 2003: 321-328.
[23] CORMEN T H, STEIN C, RIVEST R L, et al. Introduction to algorithms[M]. 2nd edition. New York: McGraw-Hill Higher Education, 1990: 4-19.
[24] CHEN X, LI L, XU G D, et al. Recommending related microblogs: a comparison between topic and WordNet based approaches[C]//Proceedings of the 26rd AAAI National Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press, 2012: 2417-2418.
[25] LIU N, LI L, XU G D, et al, Identifying domain-dependent influential microblog users: a post-feature based approach[C]//Proceedings of the 28th AAAI National Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press, 2014: 3122-3123.

基于标签的微博关键词抽取排序方法

Keyword extraction method for microblog based on hashtag

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	姬张建, 杜娜. 基于改进VariFocalNet的微小目标检测[J]. 《计算机应用》唯一官方网站, 2024, 44(7): 2200-2207.
[2]	姚迅, 秦忠正, 杨捷. 生成式标签对抗的文本分类模型[J]. 《计算机应用》唯一官方网站, 2024, 44(6): 1781-1785.
[3]	杨冬菊, 胡成富. 基于改进TextRank的科技文本关键词抽取方法[J]. 《计算机应用》唯一官方网站, 2024, 44(6): 1720-1726.
[4]	余杭, 周艳玲, 翟梦鑫, 刘涵. 基于预训练模型与标签融合的文本分类[J]. 《计算机应用》唯一官方网站, 2024, 44(3): 709-714.
[5]	马源源, 解蕾蕾, 董南, 刘娜. 考虑用户能动性和流动性的舆情传播模型[J]. 《计算机应用》唯一官方网站, 2024, 44(2): 619-627.
[6]	宋逸飞, 柳毅. 基于数据增强和标签噪声的快速对抗训练方法[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3798-3807.
[7]	刘世梁, 王义, 马应龙. 考虑社区规模不平衡的非重叠社区检测[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3396-3402.
[8]	黄雨鑫, 黄贻望, 黄辉. 基于浅层网络预测的元标签校正方法[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3364-3370.
[9]	高肇泽, 朱小飞, 项能强. 基于类别感知课程学习的半监督立场检测[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 3281-3287.
[10]	王菁怡, 李超, 宋衡, 李迪, 朱俊武. 基于随机游走算法的频谱组合拍卖机制[J]. 《计算机应用》唯一官方网站, 2023, 43(8): 2352-2357.
[11]	张忠平, 郭鑫, 张玉停, 张睿博. 基于全息图平稳分布因子的离群点检测算法[J]. 《计算机应用》唯一官方网站, 2023, 43(6): 1705-1712.
[12]	何建辉, 胡春龙, 束鑫. 基于多峰标签分布学习的多任务年龄估计方法[J]. 《计算机应用》唯一官方网站, 2023, 43(5): 1578-1583.
[13]	谭钰, 王小琴, 蓝如师, 刘振丙, 罗笑南. 基于判别性矩阵分解的多标签跨模态哈希检索[J]. 《计算机应用》唯一官方网站, 2023, 43(5): 1349-1354.
[14]	张旭, 生龙, 张海芳, 田丰, 王巍. 基于标签混淆的院前急救文本分类模型[J]. 《计算机应用》唯一官方网站, 2023, 43(4): 1050-1055.
[15]	方澄, 李贝, 韩萍, 吴琼. 基于语法依存图的中文微博细粒度情感分类[J]. 《计算机应用》唯一官方网站, 2023, 43(4): 1056-1061.