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DOI: 10.14569/IJACSA.2020.0110672
PDF

Exerting 2D-Space of Sentiment Lexicons with Machine Learning Techniques: A Hybrid Approach for Sentiment Analysis

Author 1: Muhammad Yaseen Khan
Author 2: Khurum Nazir Junejo

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 6, 2020.

  • Abstract and Keywords
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Abstract: Sentiment mining from the textual content on the web can give valuable insights for discernment, strategic decision making, targeted advertisement, and much more. Supervised machine learning (ML) approaches do not capture the sentiment inherent in the individual terms. Whereas the unsupervised sen-timent lexicon (SL) based approaches lag behind ML approaches because of a bias they have towards one sentiment than the other. In this paper, we propose a hybrid approach that uses unsuper-vised sentiment lexicons to transform the term space into a two-dimensional sentiment space on which a discriminative classifier is trained in a supervised fashion. This hybrid approach yields higher accuracy, faster training, and lower memory footprint than the ML approaches. It is more suitable for scenarios where training data is scarce. We support our claim by reporting results on six social media datasets using five sentiment lexicons and four ML algorithms.

Keywords: Hybrid approach; machine learning; sentiment analysis; sentiment lexicons; sentiment space; social media analysis

Muhammad Yaseen Khan and Khurum Nazir Junejo, “Exerting 2D-Space of Sentiment Lexicons with Machine Learning Techniques: A Hybrid Approach for Sentiment Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 11(6), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110672

@article{Khan2020,
title = {Exerting 2D-Space of Sentiment Lexicons with Machine Learning Techniques: A Hybrid Approach for Sentiment Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110672},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110672},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {6},
author = {Muhammad Yaseen Khan and Khurum Nazir Junejo}
}



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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