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DOI: 10.14569/IJACSA.2019.0100674
PDF

An Effective Framework for Tweet Level Sentiment Classification using Recursive Text Pre-Processing Approach

Author 1: Muhammad Bux Alvi
Author 2: Naeem A. Mahoto
Author 3: Mukhtiar A. Unar
Author 4: M. Akram Shaikh

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 6, 2019.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: With around 330 million people around the globe tweet 6000 times per second to express their feelings about a product, policy, service, or an event. Twitter message majorly consists of thoughts. Thoughts are mostly expressed as a text and it is an open challenge to extract some insight from free text. The scope of this work is to build an effective tweet level sentiment classification framework that may use these thoughts to know collective sentiment of the folk on a particular subject. Furthermore, this work also analyses the impact of proposed tweet level recursive text pre-processing approach on overall classification results. This work achieved up to 4 points accuracy improvement over baseline approach besides mitigating feature vector space.

Keywords: Machine learning; recursive text pre-processing; sentiment analysis; sentiment classification framework; Twitter

Muhammad Bux Alvi, Naeem A. Mahoto, Mukhtiar A. Unar and M. Akram Shaikh, “An Effective Framework for Tweet Level Sentiment Classification using Recursive Text Pre-Processing Approach” International Journal of Advanced Computer Science and Applications(IJACSA), 10(6), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100674

@article{Alvi2019,
title = {An Effective Framework for Tweet Level Sentiment Classification using Recursive Text Pre-Processing Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100674},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100674},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {6},
author = {Muhammad Bux Alvi and Naeem A. Mahoto and Mukhtiar A. Unar and M. Akram Shaikh}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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