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Advancing sentiment classification through a population game model approach.

Neha Punetha1, Goonjan Jain2

  • 1Department of Applied Mathematics, Delhi Technological University, New Delhi, India.

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|September 4, 2024
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Summary
This summary is machine-generated.

This study introduces an unsupervised computational sentiment analysis method using game theory, eliminating the need for extensive training data. The novel approach achieves high accuracy in classifying emotions across languages and domains.

Keywords:
Context scoreEmotion scorePopulation game modelSentiment classification

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Manual sentiment analysis of large digital text volumes is challenging.
  • Existing computational sentiment analysis often requires extensive machine learning and pre-training.
  • Automated tools are necessary for efficient emotion comprehension in digital content.

Purpose of the Study:

  • To propose an innovative unsupervised approach for sentiment classification.
  • To overcome limitations of existing supervised machine learning techniques.
  • To develop a language-independent sentiment analysis framework.

Main Methods:

  • Utilized game theory concepts, specifically the population game model.
  • Extracted textual features: context score and emotion score from review comments.
  • Employed lexicon databases and numeric scores within a cognitive mathematical framework.

Main Results:

  • Achieved high accuracy in sentiment classification across diverse domains (hotels, restaurants, electronics).
  • Demonstrated efficacy in both English (up to 89% accuracy) and Hindi (up to 84% accuracy).
  • Validated domain and language independence through statistical analyses.

Conclusions:

  • The proposed unsupervised, game theory-based model offers an effective alternative to traditional methods.
  • The framework is language-independent and demonstrates rationality and coherence.
  • This approach significantly advances automated sentiment analysis capabilities.