Opinion Mining, Sentiment Analysis and Emotion Understanding in Advertising: A Bibliometric Analysis
This article analyzes those works that address the relationship between sentiment analysis, opinion mining, and emotion understanding in advertising to clarify the current state of these studies, explore issues, methods, findings, themes, and gaps as well as to define their significance within the current convergence advertising research scenario.
Abstract
In the last decade, the advertising industry has experienced a quantum leap, powered by \nrecent advances in neuroscience, a large investment in artificial intelligence, and a high degree of consumer \nexpertise. Within this context, opinion mining, sentiment analysis, and emotion understanding bring us closer \nto one of the most sought-after objectives of advertising: to offer relevant ads at scale. The importance \nof studies about opinion mining, sentiment analysis, and emotion understanding in advertising has been \nrising exponentially over the last years. The peak of this new situation has been the interest of the research \ncommunity in studying the relationship between such innovations and the spread of smart and contextual \nadvertising. This article analyzes those works that address the relationship between sentiment analysis, \nopinion mining, and emotion understanding in advertising. The main objective is to clarify the current state of \nthese studies, explore issues, methods, findings, themes, and gaps as well as to define their significance within \nthe current convergence advertising research scenario. To reach such objectives, a bibliometric analysis was \nconducted, retrieving and analyzing 919 research works published between 2010 and 2019 based on results \nfrom Web of Science (WoS)