Abstract
The massive social media data bring timely, multi-dimensional and rich information. Recently, many researchers have worked on event summarization with crowdsourced social media data. While existing works mostly focus on text-based summary, they only summarize representative microblogs. Public sentiment for the event is also valuable; however, this is not explored in microblogging event summary. In this paper, we propose SentiStory, which is a multi-grained sentiment analysis and event summarization system that summarizes event from two levels: coarse-grained and fine-grained sentiment analysis. In coarse-grained analysis, it discovers microblogs which are important in sentiment, while in fine-grained analysis, it detects significant change of sentiment in the event and identifies which microblog causes the change. Specifically, the proposed system comprises two modules: (1) the microblog preprocessing module firstly reduces redundant information and extracts useful information from the microblog database, and then, it separates different aspects of the event and clusters the same aspect together in a clue. (2) The multi-grained sentiment analysis model analyzes microblogs from two levels: coarse-grained and fine-grained. We perform detailed experimental study on real dataset collected from Sina Weibo, and the results demonstrate the effectiveness of our approach.












Similar content being viewed by others
Notes
References
Lee P et al (2014) CAST: a context-aware story-teller for streamingsocial content. In: Proceedings of the 23rd ACM internationalconference on conference on information and knowledge management.ACM, Shanghai, China, pp 789–798
Chakrabarti D et al (2011) Event summarization using tweets. In:Proceedings of the fifth international AAAI conference on weblogsand social media, AAAI, Barcelona, Spain, pp 66–73
Lin C et al (2012) Generating event storylines from microblogs. In:Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, Maui, HI, USA, pp 175–184
Shou L et al (2013) Sumblr: continuous summarization of evolving tweet streams. In: Proceedings of the 36th international ACM SIGIR conference on research and development in information retrieval.ACM, Dublin, Ireland, pp 533–542
Wang C et al (2013) SentiView: sentiment analysis and visualization for internet popular topics. IEEE Trans Hum Mach Syst 43(6):620–630
Guo B et al (2015) Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm. ACM Comput Surv 48(1):1–31
Nichols J et al (2012) Summarizing sporting events using twitter. In: Proceedings of the 2012 ACM international conference on intelligent user interfaces. ACM, Lisbon, Portugal, pp 189–198
Alonso O et al (2013) Timelines as summaries of popular scheduled events. In: Proceedings of the 22nd international conference on world wide web. ACM, Rio de Janeiro, Brazil, pp 1037–1044
Corney D et al (2014) Two sides to every story: subjective event summarization of sports events using Twitter. In: Proceedings of the SoMuS ICMR 2014 workshop. Glasgow, Scotland
Zubiaga A et al (2012) Towards real-time summarization of scheduled events from twitter streams. In: Proceedings of the 23rd ACMconference on hypertext and social media. ACM, Milwaukee, WI, USA, pp 319–320
Lin H et al (2010) Multi-document summarization via budgeted maximization of submodular functions. In: Proceedings of the 2010 annual conference of the North American chapter of the association for computational linguistics. Association for ComputationalLinguistics, Los Angeles, CA, pp 912–920
Lin H et al (2011) A class of submodular functions for document summarization. In: Proceedings of the 49th annual meeting of the association for computational linguistics. Association for Computational Linguistics, Uppsala, Sweden, pp 510–520
Wang D et al (2012) Generating pictorial storylines via minimum-weight connected dominating set approximation in multi-view graphs. In: Proceedings of the 26th AAAI conference on artificial intelligence. AAAI, Toronto, ON, Canada, pp 683–689
Wang Z et al (2015) On summarization and timeline generation for evolutionary tweet streams. IEEE Trans Knowl Data Eng 27(5):1301–1315
Schinas M et al (2014) StreamGrid: summarization of large scale events using topic modelling and temporal analysis. In: Proceedings of the SoMuS ICMR 2014 workshop. Glasgow, Scotland
Chua FCT et al (2013) Automatic summarization of events from social media. In: Proceedings of the 2013 international AAAI conference on weblogs and social media, AAAI, Boston, USA
Li J et al (2011) Mssf: a multi-document summarization framework based on submodularity. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval. ACM, Beijing, China, pp 1247–1248
Zhou W et al (2014) Generating textual storyline to improve situation awareness in disaster management. In: Proceedings of the IEEE 15th international conference on information reuse and integration (IRI). IEEE, San Francisco, CA, USA, pp 585–592
Zhang J et al (2016) CrowdStory: multi-layered event storyline generation with mobile crowdsourced data. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing: adjunct. ACM, Heidelberg, Germany, pp 237–240
Tsytsarau M et al (2014) Dynamics of news events and social media reaction. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, USA, pp 901–910
Choudhary A et al (2012) Social media evolution of the Egyptian revolution. Commun ACM 55(5):74–80
Wang Z et al (2014) Investigating sentiment impact on information propagation and its evolution in microblog. In: Proceedings of the 2014 international conference on behavioral, economic, and socio-cultural computing (BESC2014). IEEE, Shanghai, China
Kempter R et al (2014) EmotionWatch: visualizing fine-grained emotions in event-related Tweets. In: Proceedings of the eighth international AAAI conference on weblogs and social media. AAAI, Ann Arbor, MI, pp 236–245
Wang R et al (2015) ASEM: mining aspects and sentiment of events from microblog. In: Proceedings of the 24th ACM international on conference on information and knowledge management. ACM, Melbourne, Australia, pp 1923–1926
Meng X et al (2012) Entity-centric topic-oriented opinion summarization in twitter. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Beijing, China, pp 379–387
Wang S et al (2016) Mining aspect-specific opinion using a holistic lifelong topic model. In: Proceedings of the 25th international conference on world wide web. ACM, Montreal, QC, Canada, pp 167–176
Dermouche M et al (2014) A joint model for topic-sentiment evolution over time. In: Proceedings of the 2014 IEEE international conference on data mining. IEEE, Shenzhen, China, pp 773–778
Das A et al (2014) Modeling opinion dynamics in social networks. In: Proceedings of the 7th ACM international conference on web search and data mining. ACM, New York, USA, pp 403–412
Akcora CG et al (2010) Identifying breakpoints in public opinion. In: Proceedings of the first workshop on social media analytics. ACM, Washington, DC, USA, pp 62–66
Hu P et al (2011) Generating breakpoint-based timeline overview for news topic retrospection. In: Proceedings of the 2011 IEEE 11th international conference on data mining. IEEE, Vancouver, Canada, pp 260–269
Jiang Y et al (2011) Topic sentiment change analysis. In: Proceedings of the 2011 international workshop on machine learning and data mining in pattern recognition. Springer, Berlin Heidelberg, New York, USA, pp 443–457
Wang D et al (2013) Detecting opinion drift from Chinese web comments based on sentiment distribution computing. In: Proceedings of the 2013 international conference on web information systems engineering. Springer, Berlin Heidelberg, Nanjing, China, pp 72–81
Arthur D et al (2007) k-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on discrete algorithms. SIAM, New Orleans, Louisiana, pp 1027–1035
Acknowledgements
This work was partially supported by the National Basic Research Program of China (No. 2015CB352400), the National Natural Science Foundation of China (Nos. 61332005, 61373119).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ouyang, Y., Guo, B., Zhang, J. et al. SentiStory: multi-grained sentiment analysis and event summarization with crowdsourced social media data. Pers Ubiquit Comput 21, 97–111 (2017). https://doi.org/10.1007/s00779-016-0977-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-016-0977-x