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Cognitive Computation

, Volume 7, Issue 2, pp 226–240 | Cite as

Word Embedding Composition for Data Imbalances in Sentiment and Emotion Classification

  • Ruifeng Xu
  • Tao Chen
  • Yunqing Xia
  • Qin LuEmail author
  • Bin Liu
  • Xuan Wang
Article

Abstract

Text classification often faces the problem of imbalanced training data. This is true in sentiment analysis and particularly prominent in emotion classification where multiple emotion categories are very likely to produce naturally skewed training data. Different sampling methods have been proposed to improve classification performance by reducing the imbalance ratio between training classes. However, data sparseness and the small disjunct problem remain obstacles in generating new samples for minority classes when the data are skewed and limited. Methods to produce meaningful samples for smaller classes rather than simple duplication are essential in overcoming this problem. In this paper, we present an oversampling method based on word embedding compositionality which produces meaningful balanced training data. We first use a large corpus to train a continuous skip-gram model to form a word embedding model maintaining the syntactic and semantic integrity of the word features. Then, a compositional algorithm based on recursive neural tensor networks is used to construct sentence vectors based on the word embedding model. Finally, we use the SMOTE algorithm as an oversampling method to generate samples for the minority classes and produce a fully balanced training set. Evaluation results on two quite different tasks show that the feature composition method and the oversampling method are both important in obtaining improved classification results. Our method effectively addresses the data imbalance issue and consequently achieves improved results for both sentiment and emotion classification.

Keywords

Sentiment analysis Emotion classification Imbalanced training Word embedding Semantic compositionality 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61300112, 61370165, 61203378), Natural Science Foundation of Guangdong Province S2013010014475, MOE Specialized Research Fund for the Doctoral Program of Higher Education 20122302120070, Open Projects Program of National Laboratory of Pattern Recognition, Shenzhen International Co-operation Research Funding GJHZ20120613110641217, Shenzhen Development and Reform Commission Grant No.[2014]1507, Shenzhen Peacock Plan Research Grant KQCX20140521144507925 and Baidu Collaborate Research Funding.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Ruifeng Xu
    • 1
  • Tao Chen
    • 1
  • Yunqing Xia
    • 2
  • Qin Lu
    • 3
    Email author
  • Bin Liu
    • 1
  • Xuan Wang
    • 1
  1. 1.Shenzhen Engineering Laboratory of Digital Stage Performance RobotHarbin Institute of Technology Shenzhen Graduate SchoolShenzhenChina
  2. 2.Research Institute of Information TechnologyTsinghua UniversityBeijingChina
  3. 3.Department of ComputingThe Hong Kong Polytechnic UniversityKowloonHong Kong

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