Foundations of statistical natural language processing
Foundations of statistical natural language processing
Capturing term dependencies using a language model based on sentence trees
Proceedings of the eleventh international conference on Information and knowledge management
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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
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
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
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Sentiment classification with interpolated information diffusion kernels
Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Sentiment retrieval using generative models
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Multilingual subjectivity analysis using machine translation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Co-training for cross-lingual sentiment classification
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Sentence based sentiment classification from online customer reviews
Proceedings of the 8th International Conference on Frontiers of Information Technology
Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews
Expert Systems with Applications: An International Journal
Deciphering word-of-mouth in social media: Text-based metrics of consumer reviews
ACM Transactions on Management Information Systems (TMIS)
Product Comparison Networks for Competitive Analysis of Online Word-of-Mouth
ACM Transactions on Management Information Systems (TMIS)
Sentiment classification: The contribution of ensemble learning
Decision Support Systems
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Sentiment classification is used to identify whether the opinion expressed in a document is positive or negative. In this paper, we present an approach to do documentary-level sentiment classification by modeling description of topical terms. The motivation of this work stems from the observation that the global document classification will benefit greatly by examining the way of a topical term to give opinion in its local sentence context. Two sentence-level sentiment description models, namely positive and negative Topical Term Description Models, are constructed for each topical term. When analyzing a document, the Topical Term Description Models generate divergence to support the classification of its sentiment at the sentence-level which in turn can be used to decide the whole document classification collectively. The results of the experiments prove that our proposed method is effective. It is also shown that our results are comparable to the state-of-art results on a publicly available movie review corpus and a Chinese digital product review corpus. This is quite encouraging to us and motivates us to have further investigation on the development of a more effective topical term related description model in the future.