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An Efficient Knowledge-Based Text Pre-processing Approach for Twitter and Google+

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Advances in Computing and Data Sciences (ICACDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1046))

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Abstract

People nowadays prefer sharing their opinions towards various products and services frequently on social networking sites (SNSs). These online reviews are huge in size and act as a goldmine for organizations to understand and monitor public reviews of their products and services. But these online reviews are highly unstructured in nature due to the presence of various linguistic features like hashtags, URLs, misspelled words, emoticons and many more. This highly unstructured data makes sentiment classification a challenging task. Hence, data pre-processing is an underlying and fundamental step in sentiment analysis. In the present work, authors have rigorously explored a series of pre-processing steps and observed that the sequence order of pre-processing steps affects the overall results. Hence, a sequence order of pre-processing steps has been proposed and implemented on two different social networks - Twitter and Google+. Twitter has been selected because of its tremendous popularity among netizens and Google+ has been selected because the domain of data for the proposed approach closely matches with the users’ interests on Google+. As the existing approach for handling data on Twitter cannot be implemented directly to handle Google+ data, a modified approach for Google+ has been suggested and implemented by the authors. In addition, some new dictionaries for handling linguistic features have been compiled and existing dictionaries have also been modified to improve pre-processing results. The proposed approach is implemented to evaluate the overall results.

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Notes

  1. 1.

    https://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdf.

  2. 2.

    https://dustn.tv/social-media-statistics/#google-plus-stats.

  3. 3.

    https://www.globalmediainsight.com/blog/google-users-statistics/.

  4. 4.

    https://nextbigwhat.com/google-plus-details-in-india/.

  5. 5.

    https://www.internetslang.com/.

  6. 6.

    https://blog.emojipedia.org/facebook-reveals-most-and-least-used-emojis/.

  7. 7.

    https://nlp.stanford.edu/software/CRF-NER.shtml.

  8. 8.

    https://spacy.io/.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Delhi, Delhi, 110007, India

    Tripti Agrawal & Archana Singhal

Authors
  1. Tripti Agrawal
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  2. Archana Singhal
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Corresponding author

Correspondence to Tripti Agrawal .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Computer Science and Engineering, Jaypee Institute of Information Technology, Waknaghat, Himachal Pradesh, India

    P.K. Gupta

  3. Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. ÚTIA AV ČR, Institute of Information Theory and Automation, Prague 8, Praha, Czech Republic

    Jan Flusser

  5. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. CSE Department, Inderprastha Engineering College, Ghaziabad, Uttar Pradesh, India

    Rekha Kashyap

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Agrawal, T., Singhal, A. (2019). An Efficient Knowledge-Based Text Pre-processing Approach for Twitter and Google+. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore. https://doi.org/10.1007/978-981-13-9942-8_36

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  • DOI: https://doi.org/10.1007/978-981-13-9942-8_36

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  • Print ISBN: 978-981-13-9941-1

  • Online ISBN: 978-981-13-9942-8

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