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Sentiment Classification with Supervised Sequence Embedding
Book chapter   Open access   Peer reviewed

Sentiment Classification with Supervised Sequence Embedding

Dmitriy Bespalov, Yanjun Qi, Bing Bai and Ali Shokoufandeh
Machine Learning and Knowledge Discovery in Databases, pp 159-174
2012
DOI: https://doi.org/10.1007/978-3-642-33460-3_16
url
https://doi.org/10.1007/978-3-642-33460-3_16View
Published, Version of Record (VoR)Maybe Open Access (Publisher Bronze),  Open

Abstract

Large-Scale Text Mining Sentiment Classification Supervised Embedding Supervised Feature Learning
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.

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17 citations in Scopus

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