Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Machine Learning - Special issue on learning with probabilistic representations
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Tabu Search
A model of textual affect sensing using real-world knowledge
Proceedings of the 8th international conference on Intelligent user interfaces
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A New Bayesian Network Structure for Classification Tasks
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
The Granular Tabu Search and Its Application to the Vehicle-Routing Problem
INFORMS Journal on Computing
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
Using appraisal groups for sentiment analysis
Proceedings of the 14th ACM international conference on Information and knowledge management
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
Intelligence and Security Informatics for International Security: Information Sharing and Data Mining (Integrated Series in Information Systems)
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
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums
ACM Transactions on Information Systems (TOIS)
Tabu search for attribute reduction in rough set theory
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Tabu Search-Enhanced Graphical Models for Classification in High Dimensions
INFORMS Journal on Computing
Feature subsumption for opinion analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Markov blankets and meta-heuristics search: sentiment extraction from unstructured texts
WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
A large-scale sentiment analysis for Yahoo! answers
Proceedings of the fifth ACM international conference on Web search and data mining
Answering queries in hybrid Bayesian networks using importance sampling
Decision Support Systems
Credit Rating Change Modeling Using News and Financial Ratios
ACM Transactions on Management Information Systems (TMIS)
Creating sentiment dictionaries via triangulation
Decision Support Systems
That is your evidence?: Classifying stance in online political debate
Decision Support Systems
Document-level sentiment classification: An empirical comparison between SVM and ANN
Expert Systems with Applications: An International Journal
An artificial neural network based approach for sentiment analysis of opinionated text
Proceedings of the 2012 ACM Research in Applied Computation Symposium
A document-level sentiment analysis approach using artificial neural network and sentiment lexicons
ACM SIGAPP Applied Computing Review
On text preprocessing for opinion mining outside of laboratory environments
AMT'12 Proceedings of the 8th international conference on Active Media Technology
More than words: Social networks' text mining for consumer brand sentiments
Expert Systems with Applications: An International Journal
Deriving market intelligence from microblogs
Decision Support Systems
Goal attainment on long tail web sites: An information foraging approach
Decision Support Systems
Potential Power and Problems in Sentiment Mining of Social Media
International Journal of Strategic Decision Sciences
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Sentiment analysis from unstructured text has witnessed a boom in interest in recent years, due to the sheer volume of online reviews and news corpora available in digital form. An accurate method for predicting sentiments could enable us, for instance, to extract opinions from the Internet and gauge online customers' preferences, which could prove valuable for economic or marketing research, for leveraging a strategic advantage for an enterprise, or for detecting cyber risk and security threats. In this paper, we propose a heuristic search-enhanced Markov blanket model that is able to capture the dependencies among words and provide a vocabulary that is adequate for the purpose of extracting sentiments. Computational results on two collections of online movie reviews and three collections of online news show that our method is able to identify a parsimonious set of predictive features, yet simultaneously yield comparable or better prediction results about sentiment orientations, than several state-of-the-art feature selection algorithms as well as sentiment prediction methods. Our results suggest that sentiments are captured by conditional dependencies among words as well as by keywords or high-frequency words.