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Sentiment analysis and spam detection in short informal text using learning classifier systems

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Abstract

Sentiment analysis of public views and spam detection from social media text messages are two challenging data analysis tasks due to short informal text. This paper investigates the performance of learning classifier systems (LCS), which are rule-based machine learning techniques, in sentiment analysis of twitter messages and movie reviews, and spam detection from SMS and email data sets. In this study, an existing LCS technique is extended by introducing a novel encoding scheme to represent classifier rules in order to handle the sparseness in feature vectors, which are generated using the term frequency inverse document frequency of word n-grams and sentiment lexicons. The obtained results show that the proposed encoding scheme smoothed the learning process and generated consistently good results in all experiments conducted in this study.

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Notes

  1. www.conecomm.com/contentmgr/showdetails.php/id/4008.

  2. http://sentiment.christopherpotts.net/tokenizing.html.

  3. http://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection.

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Acknowledgements

This work is supported by NSFC program (Nos. 61472022, 61421003), SKLSDE-2016ZX-11 and partly by the Beijing Advanced Innovation Center for Big Data and Brain Computing.

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Authors and Affiliations

  1. Advanced Innovation Center for Big Data and Brain Computing, School of Computer Science and Engineering, Beihang University (BUAA), Beijing, 100191, China

    Muhammad Hassan Arif & Jianxin Li

  2. Xtracta Ltd, Auckland, 1061, New Zealand

    Muhammad Iqbal

  3. Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy

    Kaixu Liu

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  1. Muhammad Hassan Arif
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  2. Jianxin Li
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  4. Kaixu Liu
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Correspondence to Jianxin Li.

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Communicated by V. Loia.

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Arif, M.H., Li, J., Iqbal, M. et al. Sentiment analysis and spam detection in short informal text using learning classifier systems. Soft Comput 22, 7281–7291 (2018). https://doi.org/10.1007/s00500-017-2729-x

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