A Conceptual Model for AI-Enabled Sentiment Analysis: Enhancing Brand Reputation Management in the Digital Age

• Author(s): Uloma Stella Nwabekee ; Friday Okpeke ; Abiola Ebunoluwa Onalaja
• Paper ID: 1706356
• Page: 877-900
• Published Date: 30-09-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 3 September-2024

Abstract

A conceptual model for AI-enabled sentiment analysis offers a strategic framework for enhancing brand reputation management in the digital age. As brands increasingly engage with consumers through digital channels, managing brand perception and responding to sentiment becomes critical. This review outlines a model that integrates advanced AI techniques to provide actionable insights into consumer sentiment and brand reputation. Sentiment analysis leverages natural language processing (NLP) and machine learning algorithms to analyze and interpret consumer opinions expressed in digital content. The proposed model incorporates several key components: data acquisition, sentiment classification, contextual analysis, and actionable insights. Data acquisition involves gathering user-generated content from various platforms, including social media, review sites, and forums. Advanced NLP techniques are employed to preprocess and clean this data, ensuring its quality and relevance. The sentiment classification component utilizes machine learning models, such as deep learning and ensemble methods, to categorize sentiments into positive, negative, or neutral. Contextual analysis further refines this by understanding the context in which sentiments are expressed, allowing for more nuanced insights. By integrating these insights with strategic decision-making processes, brands can enhance their reputation, improve customer satisfaction, and effectively manage their public image. This paper presents a conceptual model for using AI-enabled sentiment analysis to enhance brand reputation management in the digital age. It explores how natural language processing (NLP) and machine learning algorithms can analyze consumer feedback across digital platforms to gauge public sentiment towards brands. The model identifies key components of effective sentiment analysis, including data collection, analysis, and actionable insights generation. It also examines the role of AI in real-time reputation management and crisis response, offering strategic recommendations for brands to leverage AI for maintaining a positive public image. In conclusion, the conceptual model for AI-enabled sentiment analysis represents a powerful tool for modern brand management. By leveraging AI technologies to analyze and interpret consumer sentiment, brands can gain a competitive edge in managing their reputation in the digital landscape. This approach not only enhances the accuracy of sentiment assessments but also enables proactive and strategic responses to consumer feedback.

Keywords

AI-enabled sentiment analysis, brand reputation management, natural language processing, machine learning, contextual analysis, digital channels, actionable insights.

Citations

IRE Journals:
Uloma Stella Nwabekee , Friday Okpeke , Abiola Ebunoluwa Onalaja "A Conceptual Model for AI-Enabled Sentiment Analysis: Enhancing Brand Reputation Management in the Digital Age" Iconic Research And Engineering Journals Volume 8 Issue 3 2024 Page 877-900

IEEE:
Uloma Stella Nwabekee , Friday Okpeke , Abiola Ebunoluwa Onalaja "A Conceptual Model for AI-Enabled Sentiment Analysis: Enhancing Brand Reputation Management in the Digital Age" Iconic Research And Engineering Journals, 8(3)