acl acl2013 acl2013-115 acl2013-115-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Alexandra Balahur ; Hristo Tanev
Abstract: Nowadays, the importance of Social Media is constantly growing, as people often use such platforms to share mainstream media news and comment on the events that they relate to. As such, people no loger remain mere spectators to the events that happen in the world, but become part of them, commenting on their developments and the entities involved, sharing their opinions and distributing related content. This paper describes a system that links the main events detected from clusters of newspaper articles to tweets related to them, detects complementary information sources from the links they contain and subsequently applies sentiment analysis to classify them into positive, negative and neutral. In this manner, readers can follow the main events happening in the world, both from the perspective of mainstream as well as social media and the public’s perception on them. This system will be part of the EMM media monitoring framework working live and it will be demonstrated using Google Earth.
Apoorv Agarwal, Boyi Xie, Ilia Vovsha, Owen Rambow, and Rebecca Passonneau. Sentiment analysis of twitter data. In Proceedings of LSM 2011, LSM ’ 11, pages 30–38, 2011. Richa Bhayani Alec Go and Lei Huang. Twitter sentiment classication using distant supervision. Technical report, Technical report, Stanford University, 2009. Hila Becker, Feiyang Chen, Dan Iter, Mor Naaman, and Luis Gravano. Automatic identification and presentation of twitter content for planned events. In Proceedings of ICWSM 2011, 2011. J. Bollen, H. Mao, and X. Zeng. Twitter mood predicts the stock market. Journal of Computational Science, 2011. Xiaohua Liu, Shaodian Zhang, Furu Wei, and Ming Zhou. Recognizing Named Entities in Tweets. In Proceedings of ACL 2011, pages 359–367, Stroudsburg, PA, USA, 2011. Alexander Pak and Patrick Paroubek. Twitter based system: Using twitter for disambiguating sentiment ambiguous adjectives. In Proceedings of SemEval 2010, SemEval ’ 10, pages 436–439, 2010. John C. Platt. Sequential minimal optimization: A fast algorithm for training support vector ma- chines. Technical report, Advances in Kernel Methods - Support Vector Learning, 1998. Alan Ritter, Sam Clark, Mausam, and Oren Etzioni. Named Entity Recognition in Tweets: An Experimental Study. In Proceedings of EMNLP 2011, pages 1524–1534, Edinburgh, Scotland, UK., 2011. Hassan Saif, Yulan He, and Harith Alani. Alleviating data sparsity for twitter sentiment analysis. In Making Sense of Microposts (#MSM2012), pages 2–9, 2012. Hristo Tanev, Maud Ehrmann, Jakub Piskorski, and Vanni Zavarella. Enhancing event descriptions through twitter mining. In John G. Breslin, Nicole B. Ellison, James G. Shanahan, and Zeynep Tufekci, editors, ICWSM. The AAAI Press, 2012. Sudha Verma, Sarah Vieweg, William Corvey, Leysia Palen, James Martin, Martha Palmer, Aaron Schram, and Kenneth Anderson. Natural Language Processing to the Rescue? Extracting ”Situational Awareness”’ Tweets During Mass Emergency. In Proceedings of ICWSM 2011, pages 385–392. AAAI, 2011. 30