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A survey of pre-processing techniques to improve short-text quality: a case study on hate speech detection on twitter

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Abstract

Pre-processing plays an essential role in disambiguating the meaning of short-texts, not only in applications that classify short-texts but also for clustering and anomaly detection. Pre-processing can have a considerable impact on overall system performance; however, it is less explored in the literature in comparison to feature extraction and classification. This paper analyzes twelve different pre-processing techniques on three pre-classified Twitter datasets on hate speech and observes their impact on the classification tasks they support. It also proposes a systematic approach to text pre-processing to apply different pre-processing techniques in order to retain features without information loss. In this paper, two different word-level feature extraction models are used, and the performance of the proposed package is compared with state-of-the-art methods. To validate gains in performance, both traditional and deep learning classifiers are used. The experimental results suggest that some pre-processing techniques impact negatively on performance, and these are identified, along with the best performing combination of pre-processing techniques.

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Notes

  1. Hate speech is defined by Cambridge Dictionary as “public speech that expresses hate or encourages violence towards a person or group based on something such as race, religion, sex, or sexual orientation”.

  2. https://github.com/sloria/TextBlob

  3. http://norvig.com/spell-correct.html

  4. https://www.nltk.org/api/nltk.html

  5. https://github.com/scikit-learn/scikit-learn

  6. https://github.com/explosion/spaCy

  7. https://radimrehurek.com/gensim/

  8. https://stanfordnlp.github.io/CoreNLP/

  9. https://textblob.readthedocs.io/en/dev/

  10. https://github.com/cjlin1/liblinear

  11. https://machinelearningmastery.com/prepare-movie-review-data-sentiment-analysis/

  12. http://noisy-text.github.io/

  13. https://github.com/cbaziotis/ekphrasis

  14. https://pypi.org/project/pycontractions/

  15. https://pythonprogramming.net/lemmatizing-nltk-tutorial/

  16. https://gist.github.com/sebleier/554280

  17. http://norvig.com/spell-correct.html

  18. https://github.com/tweepy/tweepy

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Authors and Affiliations

  1. University of Sydney, Sydney, Australia

    Usman Naseem

  2. Deakin University, Geelong, Australia

    Imran Razzak & Peter W. Eklund

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  1. Usman Naseem
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  2. Imran Razzak
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  3. Peter W. Eklund
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Correspondence to Usman Naseem.

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Naseem, U., Razzak, I. & Eklund, P.W. A survey of pre-processing techniques to improve short-text quality: a case study on hate speech detection on twitter. Multimed Tools Appl 80, 35239–35266 (2021). https://doi.org/10.1007/s11042-020-10082-6

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