Foundations of statistical natural language processing
Foundations of statistical natural language processing
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Statistical significance of MUC-6 results
MUC6 '95 Proceedings of the 6th conference on Message understanding
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
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
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
Automatic recognition of German news focusing on future-directed beliefs and intentions
Computer Speech and Language
"I Know What You Feel": Analyzing the Role of Conjunctions in Automatic Sentiment Analysis
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Combining Local and Global Resources for Constructing an Error-Minimized Opinion Word Dictionary
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
A survey on sentiment detection of reviews
Expert Systems with Applications: An International Journal
CLaC and CLaC-NB: knowledge-based and corpus-based approaches to sentiment tagging
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UA-ZBSA: a headline emotion classification through web information
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
SELC: a self-supervised model for sentiment classification
Proceedings of the 18th ACM conference on Information and knowledge management
Building domain-oriented sentiment lexicon by improved information bottleneck
Proceedings of the 18th ACM conference on Information and knowledge management
Scary films good, scary flights bad: topic driven feature selection for classification of sentiment
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Proceedings of the third ACM international conference on Web search and data mining
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
MIEA: a mutual iterative enhancement approach for cross-domain sentiment classification
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A random walk algorithm for automatic construction of domain-oriented sentiment lexicon
Expert Systems with Applications: An International Journal
Sentiment analysis of citations using sentence structure-based features
HLT-SS '11 Proceedings of the ACL 2011 Student Session
Mining slang and urban opinion words and phrases from cQA services: an optimization approach
Proceedings of the fifth ACM international conference on Web search and data mining
Pulse: mining customer opinions from free text
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Automatic identification of personal insults on social news sites
Journal of the American Society for Information Science and Technology
Context-enhanced citation sentiment detection
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Detection of implicit citations for sentiment detection
ACL '12 Proceedings of the Workshop on Detecting Structure in Scholarly Discourse
Experiments in cross-lingual sentiment analysis in discussion forums
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
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We describe an extension to the technique for the automatic identification and labeling of sentiment terms described in Turney (2002) and Turney and Littman (2002). Their basic assumption is that sentiment terms of similar orientation tend to co-occur at the document level. We add a second assumption, namely that sentiment terms of opposite orientation tend not to co-occur at the sentence level. This additional assumption allows us to identify sentiment-bearing terms very reliably. We then use these newly identified terms in various scenarios for the sentiment classification of sentences. We show that our approach outperforms Turney's original approach. Combining our approach with a Naive Bayes bootstrapping method yields a further small improvement of classifier performance. We finally compare our results to precision and recall figures that can be obtained on the same data set with labeled data.