AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (1.2 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Portraying User Life Status from Microblogging Posts

State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Show Author Information

Abstract

Microblogging services provide a novel and popular communication scheme for Web users to share information and express opinions by publishing short posts, which usually reflect the users’ daily life. We can thus model the users’ daily status and interests according to their posts. Because of the high complexity and the large amount of the content of the microblog users’ posts, it is necessary to provide a quick summary of the users’ life status, both for personal users and commercial services. It is non-trivial to summarize the life status of microblog users, particularly when the summary is conducted over a long period. In this paper, we present a compact interactive visualization prototype, LifeCircle, as an efficient summary for exploring the long-term life status of microblog users. The radial visualization provides multiple views for a given microblog user, including annual topics, monthly keywords, monthly sentiments, and temporal trends of posts. We tightly integrate interactive visualization with novel and state-of-the-art microblogging analytics to maximize their advantages. We implement LifeCircle on Sina Weibo, the most popular microblogging service in China, and illustrate the effectiveness of our prototype with various case studies. Results show that our prototype makes users nostalgic and makes them reminiscent about past events, which helps them to better understand themselves and others.

Keywords

text visualizationmicrobloggingtopic modelsentiment analysiskeyword extraction

References

[1]
B. Liu, Sentiment analysis and opinion mining, Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1-90, 2012.
Crossref Google Scholar
[2]
Z. Liu, X. Chen, and M. Sun, Mining the interests of Chinese microbloggers via keyword extraction, Frontiers of Computer Science in China, vol. 6, no. 1, pp. 76-87, 2012.
Google Scholar
[3]
D. M. Blei, Probabilistic topic models, Communications of the ACM, vol. 55, no. 4, pp. 77-84, 2012.
Crossref Google Scholar
[4]
F. B. Viegas and M. Wattenberg, TIMELINES: Tag clouds and the case for vernacular visualization, Interactions, vol. 15, no. 4, pp. 49-52, 2008.
Crossref Google Scholar
[5]
G. M. Draper, Y. Livnat, and R. F. Riesenfeld, A survey of radial methods for information visualization, IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 5, pp. 759-776, 2009.
Crossref Google Scholar
[6]
A. Java, X. Song, T. Finin, and B. Tseng, Why we twitter: Understanding microblogging usage and communities, in Proc. 9th WebKDD and 1st SNA-KDD Workshop on Web Mining and Social Network Analysis, New York, USA, 2007, pp. 56-65.
Crossref
[7]
D. Zhao and M. B. Rosson, How and why people Twitter: The role that micro-blogging plays in informal communication at work, in Proc. 15th ACM Int. Conf. on Supporting Group Work, Sanibel Island, USA, 2009, pp. 243-252.
Crossref
[8]
H. Kwak, C. Lee, H. Park, and S. Moon, What is Twitter, a social network or a news media? in Proc. 19th Int. World Wide Web Conf., Raleigh, USA, 2010, pp. 591-600.
Crossref
[9]
B. Krishnamurthy, P. Gill, and M Arlitt, A few chirps about twitter, in Proc. 1st Workshop on Online Social Networks, Seattle, USA, 2008, pp. 19-24.
Crossref
[10]
M. Busch, K. Gade, B. Larson, P. Lok, S. Luckenbill, and J. Lin, Earlybird: Real-time search at Twitter, in Proc. 28th Int. Conf. on Data Engineering, Washington, USA, 2012, pp. 1360-1369.
Crossref
[11]
J. Teevan, D. Ramage, and M. R. Morris, #TwitterSearch: A comparison of microblog search and web search, in Proc. 4th Int. Conf. on Web Search and Data Mining, New York, USA, 2011, pp. 35-44.
Crossref
[12]
Z. Qu and Y. Liu, Interactive group suggesting for Twitter, in Proc. 49th Annual Meeting of the Association for Computational Linguistics, Portland, USA, 2011, pp. 519-523.
[13]
T. Sakaki, M. Okazaki, and Y. Matsuo, Earthquake shakes Twitter users: Real-time event detection by social sensors, in Proc. 19th Int. World Wide Web Conf., New York, USA, 2010, pp. 851-860.
Crossref
[14]
A. Culotta, Towards detecting influenza epidemics by analyzing Twitter messages, in Proc. 1st Workshop on Social Media Analytics, New York, USA, 2010, pp. 115-122.
Crossref
[15]
S. Petrovic, M. Osborne, and V. Lavrenko, Streaming first story detection with application to Twitter, in Proc. 11th Annual Conf. of the North American Chapter of the Association for Computational Linguistics, Los Angeles, USA, 2010, pp. 181-189.
[16]
H. Saif, Y. He, and H. Alani, Alleviating data sparsity for Twitter sentiment analysis, in Proc. 21st Int. World Wide Web Conf. Workshop on Making Sense of Microposts, Lyon, France, 2012, pp. 2-9.
[17]
L. Jiang, M. Yu, M. Zhou, X. Liu, and T. Zhao, Target-dependent Twitter sentiment classification, in Proc. 49th Annual Meeting of the Association for Computational Linguistics, Portland, USA, 2011, pp. 151-160.
[18]
K. L. Liu, W. J. Li, and M. Guo, Emoticon smoothed language models for Twitter sentiment analysis, in Proc. 26th AAAI Conf. on Artificial Intelligence, Toronto, Ontario, Canada, 2012, pp. 1678-1684.
[19]
E. Kouloumpis, T. Wilson, and J. Moore, Twitter sentiment analysis: The good the bad and the OMG!, in Proc. 5th Int. AAAI Conf. on Weblogs and Social Media, Barcelona, Spain, 2011, pp. 538-541.
[20]
H. Chien-Tung, L. Cheng-Te, and L. Shou-De, Modeling and visualizing information propagation in a micro-blogging platform, in Proc. 3rd Int. Conf. on Advances in Social Networks Analysis and Mining, Kaohsiung, Taiwan, China, 2011, pp. 328-335.
[21]
C. Li, T. Kuo, C. Ho, S. Hong, W. Lin, and S. Lin, Modeling and evaluating information propagation in a microblogging social network, Social Network Analysis and Mining, .
Crossref Google Scholar
[22]
A. Pal and S. Counts, Identifying topical authorities in microblogs, in Proc. 4th Int. Conf. on Web Search and Data Mining, New York, USA, 2011, pp. 45-54.
Crossref
[23]
K. Gimpel, N. Schneider, B. O’Connor, D. Das, D. Mills, J. Eisenstein, M. Heilman, D. Yogatama, J. Flanigan, and N. A. Smith, Part-of-speech tagging for Twitter: annotation, features, and experiments, in Proc. 49th Annual Meeting of the Association for Computational Linguistics, Portland, USA, 2011, pp. 42-47.
Crossref
[24]
T. Finin, W. Murnane, A. Karandikar, N. Keller, J. Martineau, and M. Dredze, Annotating named entities in Twitter data with crowdsourcing, in Proc. 11th Annual Conf. of the North American Chapter of the Association for Computational Linguistics Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk, Los Angeles, USA, 2010, pp. 80-88.
[25]
X. Liu, S. Zhang, F. Wei, and M. Zhou, Recognizing named entities in tweets, in Proc. 49th Annual Meeting of the Association for Computational Linguistics, Portland, USA, 2011, pp. 359-367.
[26]
A. Ritter, S. Clark, , and O. Etzioni, Named entity recognition in tweets: An experimental study, in Proc. 2011 Conf. on Empirical Methods on Natural Language Processing, Edinburgh, UK, 2011, pp. 1524-1534.
[27]
B. Han and T. Baldwin, Lexical normalisation of short text messages: makn sens a #twitter, in Proc. 49th Annual Meeting of the Association for Computational Linguistics, Portland, USA, 2011, pp. 368-378.
[28]
F. B. Viegas, S. Golder, and J. Donath, Visualizing email content: Portraying relationships from conversational histories, in Proc. 24th SIGCHI Conf. on Human Factors in Computing Systems, Montr¨¦al, Canada, 2006, pp. 979-988.
Crossref
[29]
J. Lamping and R. Rao, The hyperbolic browser: A focus+context technique for visualizing large hierarchies, Journal of Visual Languages & Computing, vol. 7, no. 1, pp. 33-55, 1996.
Crossref Google Scholar
[30]
J. Stasko and E. Zhang, Focus+context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations, in Proc. 6th IEEE Symposium on Information Visualization, Salt Lake City, USA, 2000, pp. 57-65.
[31]
J. Yang, M. O. Ward, and E. A. Rundensteiner, InterRing: An interactive tool for visually navigating and manipulating hierarchical structures, in Proc. 8th IEEE Symposium on Information Visualization, Boston, USA, 2002, pp. 77-84.
[32]
K. Andrews and H. Heidegger, Information slices: Visualising and exploring large hierarchies using cascading, semi-circular discs, in Proc. 4th IEEE Symposium on Information Visualization, Late Breaking Hot Topics, Research Triangle Park, NC, USA, 1998, pp. 9-12.
[33]
C. Collins, S. Carpendale, and G. Penn, DocuBurst: Visualizing document content using language structure, Computer Graphics Forum, vol. 28, no. 3, pp. 1039-1046, 2009.
Crossref Google Scholar
[34]
D. A. Keim, F. Mansmann, J. Schneidewind, and T. Schreck, Monitoring network traffic with radial traffic analyzer, in Proc. 1st IEEE Symposium on Visual Analytics Science and Technology, Baltimore, USA, 2006, pp. 123-128.
Crossref
[35]
L. Yarden, J. Agutter, S. Moon, R. F. Erbacher, and S. Foresti, A visualization paradigm for network intrusion detection, in Proc. 5th IEEE Workshop on Information Assurance and Security, New York, USA, 2002, pp. 30-37.
[36]
T. Barlow and P. Neville, A comparison of 2-D visualizations of hierarchies, in Proc. 7th IEEE Symposium on Information Visualization, San Diego, USA, 2001, pp. 131-138.
Crossref
[37]
R. Vliegen, J. J. van Wijk, and E. J. van der Linden, Visualizing business data with generalized treemaps, IEEE Transactions on Visualization and Computer Graphics, vol. 12, no. 5, pp. 789-796, 2006.
Crossref Google Scholar
[38]
F. B. Viegas, M. Wattenberg, and J. Feinberg, Participatory visualization with wordle, IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 1137-1144, 2009.
Crossref Google Scholar
[39]
K. Zhang and M. Sun, A stacked model based on word lattice for Chinese word segmentation and part-of-speech tagging, http://nlp.csai.tsinghua.edu.cn/thulac, 2013.
[40]
K. Toutanova, D. Klein, C. D. Manning, and Y. Singer, Feature-rich part-of-speech tagging with a cyclic dependency network, in Proc. 4th Annual Conf. of the North American Chapter of the Association for Computational Linguistics, Edmonton, Canada, 2003, pp. 173-180.
Crossref
[41]
D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent dirichlet allocation, Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.
Google Scholar
[42]
T. K. Landauer, P. W. Foltz, and D. Laham, An introduction to latent semantic analysis, Discourse Processes, vol. 25, no. 2-3, pp. 259-284, 1998.
Crossref Google Scholar
[43]
T. Hofmann, Probabilistic latent semantic indexing, in Proc. 22nd Annual Int. ACM SIGIR Conf., New York, USA, 1999, pp. 50-57.
Crossref
[44]
P. D. Turney, Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews, in Proc. 40th Annual Meeting on Association for Computational Linguistics, Denver, USA, 2002, pp. 417-424.
Crossref
[45]
J. Read, Using emoticons to reduce dependency in machine learning techniques for sentiment classification, in Proc. 43rd Annual Meeting on Association for Computational Linguistics Student Research Workshop, Ann Arbor, Michigan, USA, 2005, pp. 43-48.
Crossref
[46]
S. Aoki and O. Uchida, A method for automatically generating the emotional vectors of emoticons using weblog articles, in Proc. 10th WSEAS Int. Conf. on Applied Computer and Applied Computational Science, Stevens Point, Wisconsin, USA, 2011, pp. 132-136.
[47]
C. Ziemkiewicz and R. Kosara, Preconceptions and individual differences in understanding visual metaphors, Computer Graphics Forum, vol. 28, no. 3, pp. 911-918, 2009
Crossref Google Scholar
[48]
J. Heer and G. G. Robertson, Animated transitions in statistical data graphics, IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1240-1247, 2007.
Crossref Google Scholar
[49]
K. Koh, B. Lee, B. Kim, and J. Seo, ManiWordle: Providing flexible control over wordle, IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1190-1197, 2010.
Google Scholar
[50]
Z. Liu, Y. Zhang, E. Y. Chang, and M. Sun, PLDA+: Parallel latent dirichlet allocation with data placement and pipeline processing, ACM Transactions on Intelligent Systems and Technology. vol. 2, no. 3, pp. 1-18, 2011.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 18 Issue 2,
April 2013
Pages 182-195
DOI: 10.1109/TST.2013.6509101
Cite this article:
Tang J, Liu Z, Sun M, et al. Portraying User Life Status from Microblogging Posts. Tsinghua Science and Technology, 2013, 18(2): 182-195. https://doi.org/10.1109/TST.2013.6509101

804

Views

22

Downloads

7

Crossref

N/A

Web of Science

10

Scopus

0

CSCD

Altmetrics
Received: 07 December 2012
Accepted: 19 February 2013
Published: 30 April 2013
© The author(s) 2013
Return