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DOI: 10.14569/IJACSA.2023.0140505
PDF

An Enhanced SVM Model for Implicit Aspect Identification in Sentiment Analysis

Author 1: Halima Benarafa
Author 2: Mohammed Benkhalifa
Author 3: Moulay Akhloufi

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 5, 2023.

  • Abstract and Keywords
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Abstract: Opinion Mining or Sentiment Analysis (SA) is a key component of E-commerce applications where a vast number of reviews are generated by customers. SA operates on aspect level where the views are expressed on a specific aspect of a product and have a big influence on the customers’ choices and businesses’ reputation. Aspect Based Sentiment Analysis (ABSA) is the task of categorizing text by aspect and identifying the sentiment attributed to it. Implicit Aspect Identification (IAI) is a subtask of ABSA. This paper aims to empirically investigate how external knowledge (e.g. WordNet) is integrated into SVM model to address some of its intrinsic issues when dealing with classification. To achieve this research goal, we propose an approach to improve Support Vector Machines (SVM) model to deal with IAI. Using WordNet (WN) semantic relations, we suggest an enhancement for the SVM kernel computation. Experiments are conducted on three benchmark datasets of products, laptops, and restaurant reviews. The effects of our approach are examined and analyzed according to three criteria: (i) kernel function used, (ii) different experimental settings, and (iii) SVM behavior towards Overfitting and Underfitting. The research finding of our work is that the integration of external knowledge (e.g. WordNet) is experimentally proved to be significantly helpful to SVM classification for IAI and especially for addressing Overfitting and Underfitting that are considered as two of the main structural SVM issues. The empirical results demonstrate that our approach helps SVM (i) improve its performance for the three considered kernels and under different experimental settings, and (ii) deal better with Overfitting and Underfitting.

Keywords: Implicit aspect-based sentiment analysis; machine learning; supervised approaches; support vector machines; wordnet; lesk algorithm

Halima Benarafa, Mohammed Benkhalifa and Moulay Akhloufi, “An Enhanced SVM Model for Implicit Aspect Identification in Sentiment Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 14(5), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140505

@article{Benarafa2023,
title = {An Enhanced SVM Model for Implicit Aspect Identification in Sentiment Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140505},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140505},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {5},
author = {Halima Benarafa and Mohammed Benkhalifa and Moulay Akhloufi}
}



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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