Probabilistic latent semantic indexing
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Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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Opinion Mining and Sentiment Analysis
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MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
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ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Sentiment classification based on supervised latent n-gram analysis
Proceedings of the 20th ACM international conference on Information and knowledge management
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Proceedings of the 18th Australasian Document Computing Symposium
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In this paper, we introduce a novel approach for modeling n-grams in a latent space learned from supervised signals. The proposed procedure uses only unigram features to model short phrases (n-grams) in the latent space. The phrases are then combined to form document-level latent representation for a given text, where position of an n-gram in the document is used to compute corresponding combining weight. The resulting two-stage supervised embedding is then coupled with a classifier to form an end-to-end system that we apply to the large-scale sentiment classification task. The proposed model does not require feature selection to retain effective features during pre-processing, and its parameter space grows linearly with size of n-gram. We present comparative evaluations of this method using two large-scale datasets for sentiment classification in online reviews (Amazon and TripAdvisor). The proposed method outperforms standard baselines that rely on bag-of-words representation populated with n-gram features.