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Multi-level aspect based sentiment classification of Twitter data: using hybrid approach in deep learning.

Sadaf Hussain Janjua1, Ghazanfar Farooq Siddiqui1, Muddassar Azam Sindhu1

  • 1Department of Computer Sciences, Quaid-i-Azam University, Islamabad, Pakistan.

Peerj. Computer Science
|May 6, 2021
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Summary
This summary is machine-generated.

This study introduces a novel Multi-level Hybrid Aspect-Based Sentiment Classification (MuLeHyABSC) approach for analyzing Twitter data. The method enhances aspect-level sentiment analysis, outperforming existing techniques with improved accuracy across various datasets.

Keywords:
Aspect-based sentiment classificationFeature extractionFeature selectionHybrid approachInformation gainMulti-layer perceptionPrincipal component analysis

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Data Science

Background:

  • Social media provides vital textual data for research and organizational insights.
  • Traditional text classification often focuses on document or sentence-level analysis, missing nuanced sentiment details.
  • Aspect-based sentiment analysis is challenging, especially when identifying explicit and implicit aspects.

Purpose of the Study:

  • To perform finer-grained sentiment analysis at the aspect-level on Twitter data.
  • To propose and validate a new Multi-level Hybrid Aspect-Based Sentiment Classification (MuLeHyABSC) approach.
  • To compare the proposed method against various machine learning classifiers.

Main Methods:

  • Developed a Multi-level Hybrid Aspect-Based Sentiment Classification (MuLeHyABSC) approach.
  • Integrated feature ranking and selection methods with Artificial Neural Network (Multi-Layer Perceptron).
  • Compared MuLeHyABSC with Random Forest, Support Vector Classifier, and seven other classifiers.

Main Results:

  • The proposed MuLeHyABSC approach demonstrated superior performance compared to baseline methods.
  • Achieved high accuracy rates across multiple Twitter datasets: 78.99%, 84.09%, 80.38%, 82.37%, and 84.72%.
  • Validated the efficiency and enhanced functionality of the hybrid aspect-based text classification.

Conclusions:

  • The MuLeHyABSC approach significantly enhances aspect-based text classification for social media data.
  • The hybrid method offers improved sentiment classification accuracy over existing approaches.
  • This research provides a more nuanced understanding of user opinions by analyzing explicit and implicit aspects.