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Twitter Feature Selection and Classification Using Support Vector Machine for Aspect-Based Sentiment Analysis

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9799)

Abstract

In this paper, with regards to aspect-based sentiment classification accuracy problem, we propose a Principal Component Analysis (PCA) feature selection method that can determine the most relevant set of features for aspect-based sentiment classification. Feature selection helps to reduce redundant features and remove irrelevant features which affect classifier accuracy. In this paper we present a method for feature selection for twitter aspect-based sentiment classification based on Principal Component Analysis (PCA). PCA is combined with Sentiwordnet lexicon-based method which is incorporated with Support Vector Machine (SVM) learning framework to perform the classification. Experiments on our own Hate Crime Twitter Sentiment (HCTS) and benchmark Stanford Twitter Sentiment (STS) datasets yields accuracies of 94.53 % and 97.93 % respectively. The comparisons with other statistical feature selection methods shows that our proposed approach shows promising results in improving aspect-based sentiment classification performance.

Keywords

  • Twitter
  • Aspect-based feature extraction
  • Aspect-based sentiment classification
  • Feature selection
  • Principal component analysis
  • Support vector machine

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Acknowledgments

The authors wish to thank Universiti Teknologi Malaysia (UTM) under Research University Grant Vot- 02G31 and Ministry of Higher Education Malaysia (MOHE) under the Fundamental Research Grant Scheme (FRGS Vot-4F551) for the completion of the research.

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Correspondence to Ali Selamat .

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Zainuddin, N., Selamat, A., Ibrahim, R. (2016). Twitter Feature Selection and Classification Using Support Vector Machine for Aspect-Based Sentiment Analysis. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_23

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  • DOI: https://doi.org/10.1007/978-3-319-42007-3_23

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