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Updated: Jun 4, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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Sentiment analysis of tweets employing convolutional neural network optimized by enhanced gorilla troops optimization

Fang Li1, Jialing Li2, Francis Abza3,4

  • 1Global Business School, Chongqing College Of International Business And Economics, Chongqing, 401520, China.

Scientific Reports
|January 4, 2025
PubMed
Summary

This study introduces a novel Convolutional Neural Network (CNN) optimized by the Enhanced Gorilla Troops Optimization Algorithm (EGTO) for accurate sentiment analysis of social media text. The CNN-EGTO model effectively overcomes challenges like abbreviations and spelling errors, achieving high performance in classifying tweet polarity.

Keywords:
Convolutional neural networkEnhanced gorilla optimization algorithmSentiment analysisSupervised learningTwitter

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Computational Linguistics

Background:

  • Sentiment analysis is crucial for understanding public opinion on social media.
  • Conventional methods struggle with noisy data like abbreviations, misspellings, and varying tweet lengths.
  • Social media posts can unconsciously reveal users' underlying emotions and psychological states.

Purpose of the Study:

  • To develop an advanced sentiment analysis model capable of handling challenges in social media data.
  • To improve the accuracy and efficiency of classifying tweet sentiment as positive or negative.
  • To introduce a novel optimization algorithm for enhancing Convolutional Neural Network (CNN) performance.

Main Methods:

  • Utilized a Convolutional Neural Network (CNN) architecture.
  • Optimized the CNN using the Enhanced Gorilla Troops Optimization Algorithm (EGTO).
  • Evaluated the model on two datasets from SemEval-2016, with manually verified tweet polarities.

Main Results:

  • The CNN-EGTO model achieved high performance metrics for positive sentiment classification: 98% accuracy, 95% precision, 98% recall, and 96.47% F1-score.
  • For negative sentiment classification, the model attained 97% precision, 96% recall, 98% accuracy, and 97.49% F1-score.
  • The proposed model demonstrated superior performance compared to existing methods in terms of efficiency and accuracy.

Conclusions:

  • The CNN-EGTO model effectively addresses the limitations of conventional approaches in sentiment analysis.
  • The optimized CNN provides a robust and efficient solution for determining the polarity of social media text.
  • This research contributes a high-performing model for nuanced sentiment analysis in challenging datasets.