AI-Powered Brand Perception Analysis: A Neural Network Approach for Company Branding Classification

Brand perception plays a crucial role in a company's market position, influencing customer trust, loyalty, and decision-making. Traditional methods of analysing brand perception rely on surveys and sentiment analysis, which can be timeconsuming and subjective. This paper proposes an AI-driven approach using neural networks to classify and analyse company branding. By leveraging Convolutional Neural Networks (CNNs) for visual branding and Recurrent Neural Networks (RNNs) or Transformers for text-based brand perception, we demonstrate a robust classification framework. The study explores datasets, model architectures, and evaluation metrics to assess AI's effectiveness in brand classification. The results indicate that AIpowered branding analysis can significantly enhance real-time brand monitoring and sentiment assessment

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-06-09
DOI
https://doi.org/10.22214/ijraset.2025.72177
Primary Topic
Consumer Behavior in Brand Consumption and Identification
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Powered Brand Perception Analysis: A Neural Network Approach for Company Branding Classification

Shekhar Saxena
1 links
International Journal for Research in Applied Science and Engineering Technology
Consumer Behavior in Brand Consumption and Identification
article

AI-Powered Brand Perception Analysis: A Neural Network Approach for Company Branding Classification

Shekhar Saxena
article en

Abstract

Brand perception plays a crucial role in a company's market position, influencing customer trust, loyalty, and decision-making. Traditional methods of analysing brand perception rely on surveys and sentiment analysis, which can be timeconsuming and subjective. This paper proposes an AI-driven approach using neural networks to classify and analyse company branding. By leveraging Convolutional Neural Networks (CNNs) for visual branding and Recurrent Neural Networks (RNNs) or Transformers for text-based brand perception, we demonstrate a robust classification framework. The study explores datasets, model architectures, and evaluation metrics to assess AI's effectiveness in brand classification. The results indicate that AIpowered branding analysis can significantly enhance real-time brand monitoring and sentiment assessment

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(6)
Peace, Justice and strong institutions
Openalex Percentile: Top 84%
Consumer Behavior in Brand Consumption and Identification
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