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Erschienen in:
Learning Sparse Overcomplete Word Vectors Without Intermediate Dense Representations Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Weakly Supervised Feature Compression Based Topic Model for Sentiment Classification

verfasst von : Yan Hu, Xiaofei Xu, Li Li

Erschienen in: Knowledge Science, Engineering and Management

Verlag: Springer International Publishing

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Abstract

Sentiment classification aims to use automatic tools to explore the subjective information like opinions and attitudes from user comments. Most of existing methods are centered on the semantic relationships and the extraction of syntactic feature, while the document topic feature is ignored. In this paper, a weakly supervised hierarchical model called external knowledge-based Latent Dirichlet Allocation (ELDA) is proposed to extract document topic feature. First of all, we take advantage of ELDA to compress document feature and increase the polarity weight of document topic feature. And then, we train a classifier based on the topic feature using SVM. Experiment results on one English dataset and one Chinese dataset show that our method can outperform the state-of-the-art models by at least \(4\%\) in terms of accuracy.

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Vorheriges Kapitel A Study of Distributed Semantic Representations for Automated Essay Scoring
Nächstes Kapitel An Effective Gated and Attention-Based Neural Network Model for Fine-Grained Financial Target-Dependent Sentiment Analysis
Fußnoten
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Metadaten
Titel
Weakly Supervised Feature Compression Based Topic Model for Sentiment Classification
verfasst von
Yan Hu
Xiaofei Xu
Li Li
Copyright-Jahr
2017
Buch
Knowledge Science, Engineering and Management

Print ISBN: 978-3-319-63557-6

Electronic ISBN: 978-3-319-63558-3

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-63558-3_3