Sentiment Classification with Graph Sparsity Regularization
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- 作者：Xin-Yu Dai (14)
Chuan Cheng (14)
Shujian Huang (14)
Jiajun Chen (14)
14. National Key Laboratory for Novel Software Technology ; Nanjing University ; Nanjing ; 210023 ; China - 关键词：Text Graph Representation ; Graph Regularization ; Sentiment Classification
- 刊名：Lecture Notes in Computer Science
- 出版年：2015
- 出版时间：2015
- 年：2015
- 卷：9042
- 期：1
- 页码：140-151
- 全文大小：1,778 KB
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- 丛书名：978-3-319-18116-5
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence and Robotics
Computer Communication Networks
Software Engineering
Data Encryption
Database Management
Computation by Abstract Devices
Algorithm Analysis and Problem Complexity - 出版者：Springer Berlin / Heidelberg
- ISSN：1611-3349
文摘
Text representation is a preprocessing step in building a classifier for sentiment analysis. But in vector space model (VSM) or bag-of -features (BOF) model, features are independent of each other when to learn a classifier model. In this paper, we firstly explore the text graph structure which can represent the structural features in natural language text. Different to the BOF model, by directly embedding the features into a graph, we propose a graph sparsity regularization method which can make use of the the graph embedded features. Our proposed method can encourage a sparse model with a small number of features connected by a set of paths. The experiments on sentiment classification demonstrate our proposed method can get better results comparing with other methods. Qualitative discussion also shows that our proposed method with graph-based representation is interpretable and effective in sentiment classification task.