Abstract
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.
This research was supported by NSFC (61472183, 61170181).
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Dai, XY., Cheng, C., Huang, S., Chen, J. (2015). Sentiment Classification with Graph Sparsity Regularization. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9042. Springer, Cham. https://doi.org/10.1007/978-3-319-18117-2_11
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DOI: https://doi.org/10.1007/978-3-319-18117-2_11
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