Artificial Intelligence and Sentiment Analysis: Advancing Opportunities in Marketing Research

Abstract

Background and Purpose: Sentiment analysis, also known as opinion mining, is a computational technique that identifies, extracts, and classifies subjective information from text data to determine emotional tone and consumer attitudes toward a topic, product, or brand. In marketing research, it has emerged as a promising tool for analyzing large volumes of user-generated content from sources such as social media, product reviews, and online forums. Recent advancements in Artificial Intelligence have further enhanced its effectiveness, making it easier to collect vast amounts of timely and valuable customer feedback and information across various platforms. While widely applied in marketing practice, the integration of sentiment analysis into academic research remains limited. Therefore, this paper explores the potential of sentiment analysis as a complementary or alternative method to traditional online surveys and Likert-scale questionnaires, which often present limitations such as recall bias, low response rates, and restricted emotional nuance. The aim is to assess the opportunities and challenges related to this methodological innovation in marketing research. Methods: This conceptual review synthesizes findings from a broad body of scholarly research articles. It categorizes the core applications of sentiment analysis, discusses dominant analytical techniques, and identifies recurring methodological and ethical challenges related to its use. Results/findings: Sentiment analysis offers real-time, large-scale, and unsolicited consumer insight, making it especially valuable for tracking market trends, brand perception, and emotional response. However, technical complexities such as sarcasm detection and sentiment ambiguity along with concerns over algorithmic bias and data ethics, remain substantial barriers to its adoption by researchers. Conclusions/ Implications: The paper proposes a structured roadmap for implementing sentiment analysis, especially for non-technical researchers. It emphasizes the importance of methodological alignment, data validation, and ethical consideration. By overcoming traditional survey limitations, AI-driven sentiment analysis can enhance the depth, authenticity, and reliability of consumer insights. This approach aligns with the principles of Marketing 5.0 that leverages advanced technologies such as artificial intelligence, machine learning, and big data to create more personalized, predictive, and human-centric marketing experiences.