Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Word association norms, mutual information, and lexicography
Computational Linguistics
WordNet: a lexical database for English
Communications of the ACM
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
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
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Determining the semantic orientation of terms through gloss classification
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
Hownet And the Computation of Meaning
Hownet And the Computation of Meaning
Extracting semantic orientations of words using spin model
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums
ACM Transactions on Information Systems (TOIS)
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
A machine learning approach to sentiment analysis in multilingual Web texts
Information Retrieval
Optimizing modularity to identify semantic orientation of Chinese words
Expert Systems with Applications: An International Journal
A classification-based review recommender
Knowledge-Based Systems
IEEE Intelligent Systems
Sentiment in short strength detection informal text
Journal of the American Society for Information Science and Technology
Selecting Attributes for Sentiment Classification Using Feature Relation Networks
IEEE Transactions on Knowledge and Data Engineering
Opinion word expansion and target extraction through double propagation
Computational Linguistics
SentiFul: A Lexicon for Sentiment Analysis
IEEE Transactions on Affective Computing
Estimating sequential bias in online reviews: A Kalman filtering approach
Knowledge-Based Systems
Top-N news recommendations in digital newspapers
Knowledge-Based Systems
Extended information inference model for unsupervised categorization of web short texts
Journal of Information Science
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Sentiment analysis continues to be a most important research problem due to its abundant applications. Identifying the semantic orientation of subjective terms (words or phrases) is a fundamental task for sentiment analysis. In this paper, we propose a new method for identifying the semantic orientation of subjective terms to perform sentiment analysis. The method takes a classification approach that is based on a novel semantic orientation representation model called S-HAL (Sentiment Hyperspace Analogue to Language). S-HAL basically produces a set of weighted features based on surrounding words, and characterizes the semantic orientation information of words via a specific feature space. Because the method incorporates the idea underlying HAL and the hypothesis verified by the method of semantic orientation inference from pointwise mutual information (SO-PMI), it can quickly and accurately identify the semantic orientation of terms without the use of an Internet search engine. The results of an empirical evaluation show that our method outperforms other known methods.