Mining newsgroups using networks arising from social behavior
WWW '03 Proceedings of the 12th international conference on World Wide Web
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Document-Word Co-regularization for Semi-supervised Sentiment Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Get out the vote: determining support or opposition from congressional floor-debate transcripts
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
Graph ranking for sentiment transfer
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Opinion graphs for polarity and discourse classification
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
Constraint-driven rank-based learning for information extraction
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Micro-blogging Sentiment Detection by Collaborative Online Learning
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Target-dependent Twitter sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Content vs. context for sentiment analysis: a comparative analysis over microblogs
Proceedings of the 23rd ACM conference on Hypertext and social media
Predicting collective sentiment dynamics from time-series social media
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
User-sentiment topic model: refining user's topics with sentiment information
Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics
Learning for microblogs with distant supervision: political forecasting with Twitter
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Mining topic-level opinion influence in microblog
Proceedings of the 21st ACM international conference on Information and knowledge management
Semantic sentiment analysis of twitter
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Assembling the optimal sentiment classifiers
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
Exploiting social relations for sentiment analysis in microblogging
Proceedings of the sixth ACM international conference on Web search and data mining
Models and algorithms for social influence analysis
Proceedings of the sixth ACM international conference on Web search and data mining
Identifying same wavelength groups from twitter: a sentiment based approach
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Sentiment and topic analysis on social media: a multi-task multi-label classification approach
Proceedings of the 5th Annual ACM Web Science Conference
Inferring social roles and statuses in social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative boosting for activity classification in microblogs
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic graphical model for brand reputation assessment in social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
I act, therefore I judge: network sentiment dynamics based on user activity change
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Learning to predict reciprocity and triadic closure in social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Towards social imagematics: sentiment analysis in social multimedia
Proceedings of the Thirteenth International Workshop on Multimedia Data Mining
Power walk: revisiting the random surfer
Proceedings of the 18th Australasian Document Computing Symposium
Listening to the crowd: automated analysis of events via aggregated twitter sentiment
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We show that information about social relationships can be used to improve user-level sentiment analysis. The main motivation behind our approach is that users that are somehow "connected" may be more likely to hold similar opinions; therefore, relationship information can complement what we can extract about a user's viewpoints from their utterances. Employing Twitter as a source for our experimental data, and working within a semi-supervised framework, we propose models that are induced either from the Twitter follower/followee network or from the network in Twitter formed by users referring to each other using "@" mentions. Our transductive learning results reveal that incorporating social-network information can indeed lead to statistically significant sentiment classification improvements over the performance of an approach based on Support Vector Machines having access only to textual features.