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

Abstract

Customers perceptions of a business contribute significantly to its performance. Having a positive brand image cultivates credibility, retention, and improved revenues. In the past, businesses relied on rudimentary methods such as reviews and surveys which were manual and time-consuming, and often, inaccurate and biased. This study looks at how AI can streamline time-consuming processes and improve efficiency. It employs one type of AI to process graphical customer brand images such as logos, and another type to analyze customer sentiment in text form like reviews or social media comments. In the tests where use of AI was implemented, this technique outperformed older methods in both time and accuracy, confirming its usefulness in real-time brand sentiment analysis.

Introduction

In today’s competitive market, branding plays a crucial role in shaping customer trust and loyalty. Traditionally, companies relied on surveys and manual analysis to assess public perception, but these methods were slow, biased, and limited. With the rise of AI and deep learning, brand analytics has become more efficient and accurate, enabling real-time analysis of large volumes of data from social media, reviews, advertisements, and other digital platforms.

Different AI models serve specific functions in brand analysis. Convolutional Neural Networks (CNNs) are effective for image-based tasks such as logo and advertisement recognition, while Recurrent Neural Networks (RNNs), LSTMs, and Transformer models like BERT and GPT are used for text-based sentiment analysis. The study proposes a multimodal learning framework that integrates both text and image analysis to enhance sentiment detection, monitor changes in brand perception, and provide actionable insights. Experimental results show that multimodal AI improves classification accuracy by 15–20% compared to single-modality models.

The research also evaluates model efficiency in terms of prediction delay and computational resource usage. While Transformers offer faster parallel processing, they require significant computational power. Optimization techniques such as pruning, knowledge distillation, and quantization help reduce computational demands.

Real-world companies such as Coca-Cola, Amazon, and Nike have successfully adopted AI for sentiment analysis, customer engagement tracking, and brand strategy improvement. However, challenges remain, particularly regarding data bias and ethical concerns. Biased datasets can misrepresent certain demographics, leading to flawed brand insights. To address this, businesses should ensure diverse datasets, use bias-detection tools, and adopt Explainable AI (XAI) to increase transparency and trust.

Overall, AI-powered multimodal branding analysis represents a powerful and evolving approach for understanding and managing brand perception effectively and ethically.

Conclusion

This study demonstrates how AI can improve data errors and efficiency in our assessment of a brand\'s customer sentiment. Additionally, the analysis suggests that AI is capable of classifying and categorizing data and offering superior insights compared to more conventional methods. Insightfully educating businesses about their brand has advanced significantly with the use of visual data, such as brand logos, branded advertising, and other branded visual artifacts, in addition to textual data, such as reviews and text posts on social media. The time savings are significant, but it also raises doubts about AI\'s capacity to handle massive data sets at once. Future improvements can be made in a lot of areas. Additional data, such as audio or video recordings or even interactive engagement and interactions that incorporate a transcultural learning component of brand interaction, may be incorporated by researchers. In the future, this will enable the A.I. to become much more intelligent and, consequently, more legitimate from a wider range of perspectives. Creating outputs that help users and consumers comprehend how artificial intelligence processes data and makes decisions is another crucial factor to take into account. Businesses are more inclined to trust this method of data collection when they can observe how A.I. arrived at its conclusions. Additionally, being able to understand the logic behind AI\'s conclusions can assist businesses in considering and planning for their goals.

References

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Copyright

Copyright © 2026 Manish Joshi, Anmol Sing, Prashant Mehta, Sachin Yadav, Siddharth Saxena, Saptarshi Dhameliya, Khushi Patel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.