Making large-scale support vector machine learning practical
Advances in kernel methods
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A novel scheme for domain-transfer problem in the context of sentiment analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Learning to identify emotions in text
Proceedings of the 2008 ACM symposium on Applied computing
Textual Affect Sensing for Sociable and Expressive Online Communication
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Inter-coder agreement for computational linguistics
Computational Linguistics
Determining the Polarity and Source of Opinions Expressed in Political Debates
CICLing '09 Proceedings of the 10th International Conference on Computational Linguistics and Intelligent Text Processing
A survey on sentiment detection of reviews
Expert Systems with Applications: An International Journal
Feature subsumption for opinion analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Data mining emotion in social network communication: Gender differences in MySpace
Journal of the American Society for Information Science and Technology
Genre-based paragraph classification for sentiment analysis
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
An unobtrusive behavioral model of "gross national happiness"
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Characterizing debate performance via aggregated twitter sentiment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exploring the sentiment strength of user reviews
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Sentiment in short strength detection informal text
Journal of the American Society for Information Science and Technology
Journal of the American Society for Information Science and Technology
Automatic construction of a context-aware sentiment lexicon: an optimization approach
Proceedings of the 20th international conference on World wide web
SentiFul: A Lexicon for Sentiment Analysis
IEEE Transactions on Affective Computing
Lexicon-based methods for sentiment analysis
Computational Linguistics
Happiness is assortative in online social networks
Artificial Life
Pulse: mining customer opinions from free text
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Combining lexicon and learning based approaches for concept-level sentiment analysis
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
Political polarization and popularity in online participatory media: an integrated approach
Proceedings of the first edition workshop on Politics, elections and data
Proceedings of the 2012 international workshop on Socially-aware multimedia
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Modelling emotional trajectories of individuals in an online chat
MATES'12 Proceedings of the 10th German conference on Multiagent System Technologies
Damping sentiment analysis in online communication: discussions, monologs and dialogs
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Twitter stream analysis in Spanish
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
A sentiment-enhanced personalized location recommendation system
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Analyzing and predicting viral tweets
Proceedings of the 22nd international conference on World Wide Web companion
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Combining strengths, emotions and polarities for boosting Twitter sentiment analysis
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
Penguins in sweaters, or serendipitous entity search on user-generated content
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Sentiment analysis is concerned with the automatic extraction of sentiment-related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twitter in particular. Most sentiment analysis algorithms are not ideally suited to this task because they exploit indirect indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms used to process social web texts can identify spurious sentiment patterns caused by topics rather than affective phenomena. This article assesses an improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment. The results from six diverse social web data sets (MySpace, Twitter, YouTube, Digg, RunnersWorld, BBCForums) indicate that SentiStrength 2 is successful in the sense of performing better than a baseline approach for all data sets in both supervised and unsupervised cases. SentiStrength is not always better than machine-learning approaches that exploit indirect indicators of sentiment, however, and is particularly weaker for positive sentiment in news-related discussions. Overall, the results suggest that, even unsupervised, SentiStrength is robust enough to be applied to a wide variety of different social web contexts.