@article{Atzmueller:12,
  author = {Atzmueller, Martin},
  interhash = {ce21e72d189207dbee58420af81efca8},
  intrahash = {a66dc503e2f90d0a484e0dbef5febcd3},
  journal = {Informatik Spektrum},
  number = 2,
  pages = {132-135},
  title = {Mining Social Media},
  volume = 35,
  year = 2012
}

@article{Atzmueller:12c,
  author = {Atzmueller, Martin},
  interhash = {0b20c1d53d5df05326d594726273c2fb},
  intrahash = {7b616e64994893a2aad95b5ad95db662},
  journal = {WIREs: Data Mining and Knowledge Discovery},
  title = {{Mining Social Media: Key Players, Sentiments, and Communities}},
  volume = {In Press},
  year = 2012
}

@inproceedings{brew2010using,
  abstract = {Tracking sentiment in the popular media has long been of interest to media analysts and pundits. With the availability of news content via online syndicated feeds, it is now possible to automate some aspects of this process. There is also great potential to crowdsource Crowdsourcing is a term, sometimes associated with Web 2.0 technologies, that describes outsourcing of tasks to a large often anonymous community. much of the annotation work that is required to train a machine learning system to perform sentiment scoring. We describe such a system for tracking economic sentiment in online media that has been deployed since August 2009. It uses annotations provided by a cohort of non-expert annotators to train a learning system to classify a large body of news items. We report on the design challenges addressed in managing the effort of the annotators and in making annotation an interesting experience.},
  acmid = {1860997},
  address = {Amsterdam, The Netherlands, The Netherlands},
  author = {Brew, Anthony and Greene, Derek and Cunningham, Pádraig},
  booktitle = {Proceedings of the 19th European Conference on Artificial Intelligence},
  editor = {Coelho, Helder and Studer, Rudi and Wooldridge, Michael},
  interhash = {90650749ea1084b729710d37b5865b72},
  intrahash = {9643e3c5729886b0b4e85cb3d3d704f5},
  isbn = {978-1-60750-605-8},
  numpages = {6},
  pages = {145--150},
  publisher = {IOS Press},
  series = {Frontiers in Artificial Intelligence and Applications},
  title = {Using Crowdsourcing and Active Learning to Track Sentiment in Online Media},
  url = {http://dl.acm.org/citation.cfm?id=1860967.1860997},
  volume = 215,
  year = 2010
}

@inproceedings{iswcTwitter2011,
  address = {Bonn, Germany},
  author = {Saif, Hassan and He, Yulan and Alani, Harith},
  booktitle = {The 10th International Semantic Web Conference (ISWC)},
  interhash = {4cb80cbd6980b69e6b85b403cd548b91},
  intrahash = {073f558b682ff264de2af731da8a3a3a},
  title = {Semantic Smoothing for Twitter Sentiment Analysis},
  year = 2011
}

@inproceedings{default,
  author = {Pak, Alexander and Paroubek, Patrick},
  interhash = {ac930b0459a3c8a2fc2d74c52a475026},
  intrahash = {ba1358f07702423b60c9e94f8aa5985c},
  issue = {10},
  pages = {1320-1326},
  title = {Twitter as a Corpus for Sentiment Analysis and Opinion Mining},
  url = {http://www.mendeley.com/research/twitter-corpus-sentiment-analysis-opinion-mining-18/},
  volume = 2010,
  year = 2010
}

@misc{Kim2012,
  abstract = {  Sentiment analysis predicts the presence of positive or negative emotions in a text document. In this paper we consider higher dimensional extensions of the sentiment concept, which represent a richer set of human emotions. Our approach goes beyond previous work in that our model contains a continuous manifold rather than a finite set of human emotions. We investigate the resulting model, compare it to psychological observations, and explore its predictive capabilities. Besides obtaining significant improvements over a baseline without manifold, we are also able to visualize different notions of positive sentiment in different domains. },
  author = {Kim, Seungyeon and Li, Fuxin and Lebanon, Guy and Essa, Irfan},
  interhash = {78c5eda9e1ef2780d70234dc4942203f},
  intrahash = {d169c08d5241a0912f3d60c97d87e2c0},
  note = {cite arxiv:1202.1568Comment: 15 pages, 7 figures},
  title = {Beyond Sentiment: The Manifold of Human Emotions},
  url = {http://arxiv.org/abs/1202.1568},
  year = 2012
}

@article{Go_Huang_Bhayani_2009,
  author = {Go, A and Huang, L and Bhayani, R},
  interhash = {c462bf3fa792403429b46ec83efc2d06},
  intrahash = {21e712d455a36a1125bd9bfe6c9383a8},
  journal = {Entropy},
  number = {June},
  pages = 17,
  publisher = {Association for Computational Linguistics},
  title = {Sentiment Analysis of Twitter Data},
  url = {http://nlp.stanford.edu/courses/cs224n/2009/fp/3.pdf},
  volume = 2009,
  year = 2009
}

@article{opinion.review.2010,
  author = {Liu, Bing},
  editor = {Indurkhya, N. and Damerau, F. J.},
  interhash = {d95273d3139fee537a2e08a2fc5b4b38},
  intrahash = {098caba4af7a344db7a62c5d10748727},
  journal = {Handbook of Natural Language Processing},
  title = {Sentiment Analysis and Subjectivity},
  volume = {2nd ed},
  year = 2010
}

@inproceedings{yanbe2007social,
  abstract = {Social bookmarking is an emerging type of a Web service that helps users share, classify, and discover interesting resources. In this paper, we explore the concept of an enhanced search, in which data from social bookmarking systems is exploited for enhancing search in the Web. We propose combining the widely used link-based ranking metric with the one derived using social bookmarking data. First, this increases the precision of a standard link-based search by incorporating popularity estimates from aggregated data of bookmarking users. Second, it provides an opportunity for extending the search capabilities of existing search engines. Individual contributions of bookmarking users as well as the general statistics of their activities are used here for a new kind of a complex search where contextual, temporal or sentiment-related information is used. We investigate the usefulness of social bookmarking systems for the purpose of enhancing Web search through a series of experiments done on datasets obtained from social bookmarking systems. Next, we show the prototype system that implements the proposed approach and we present some preliminary results.},
  address = {New York, NY, USA},
  author = {Yanbe, Yusuke and Jatowt, Adam and Nakamura, Satoshi and Tanaka, Katsumi},
  booktitle = {JCDL '07: Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries},
  doi = {10.1145/1255175.1255198},
  interhash = {13ebfc0942b5908890c3caaa7046fe50},
  intrahash = {9e3f8071d757c492055744cf03ff4a55},
  isbn = {978-1-59593-644-8},
  location = {Vancouver, BC, Canada},
  pages = {107--116},
  publisher = {ACM},
  title = {Can social bookmarking enhance search in the web?},
  url = {http://portal.acm.org/citation.cfm?id=1255175.1255198},
  year = 2007
}

@article{pang2008opinion,
  abstract = {An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.},
  address = {Hanover, MA, USA},
  author = {Pang, Bo and Lee, Lillian},
  doi = {10.1561/1500000011},
  interhash = {7bfd8b20ea5f9fb76e96d71c3155c50c},
  intrahash = {4d0e1a6268b3d8a119aaf2b0c2cb5154},
  issn = {1554-0669},
  journal = {Foundations and Trends in Information Retrieval},
  month = jan,
  number = {1-2},
  pages = {1--135},
  publisher = {Now Publishers Inc.},
  title = {Opinion Mining and Sentiment Analysis},
  url = {http://portal.acm.org/citation.cfm?id=1454712},
  volume = 2,
  year = 2008
}

@article{Pang2008,
  author = {Pang, Bo and Lee, Lillian},
  date = {July 2008},
  interhash = {7bfd8b20ea5f9fb76e96d71c3155c50c},
  intrahash = {236d4f703fda3dd9457863f28eda56cb},
  isbn = {978-1-60198-150-9},
  journal = {Foundations and Trends® in Information Retrieval},
  number = {1-2},
  pages = {1-135},
  tech = {Now publishers},
  title = {Opinion mining and sentiment analysis},
  url = {http://www.cs.cornell.edu/home/llee/omsa/omsa-published.pdf},
  volume = 2,
  year = 2008
}