Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

Using Biomedical Technologies to Inform Economic Modeling: Challenges and Opportunities for Improving Analysis of Environmental Policies

Advances in biomedical technology have irrevocably jarred open the black box of human decision making, offering social scientists the potential to validate, reject, refine and redefine the individual models of resource allocation that form the foundation of modern economics. In this paper we (1) provide a comprehensive overview of the biomedical methods that may be harnessed by economists and other social scientists to better understand the economic decision making process; (2) review research that utilizes these biomedical methods to illuminate fundamental aspects of the decision making process; and (3) summarize evidence from this literature concerning the basic tenants of neoclassical utility that are often invoked for positive welfare analysis of environmental policies. We conclude by raising questions about the future path of policy related research and the role biomedical technologies will play in defining that path.neuroeconomics, neuroscience, brain imaging, genetics, welfare economics, utility theory, biology, decision making, preferences, Institutional and Behavioral Economics, Research Methods/ Statistical Methods, D01, D03, D6, D87,

Unraveling neural pathways of political engagement: bridging neuromarketing and political science for understanding voter behavior and political leader perception

Political neuromarketing is an interdisciplinary field that combines marketing, neuroscience, and psychology to understand voter behavior and political leader perception. This interdisciplinary field offers novel techniques to understand complex phenomena such as voter engagement, political leadership, and party branding. This study aims to understand the neural activation patterns of voters when they are exposed to political leaders using functional near-infrared spectroscopy (fNIRS) and machine learning methods. We recruited participants and recorded their brain activity using fNIRS when they were exposed to images of different political leaders. This neuroimaging method (fNIRS) reveals brain regions central to brand perception, including the dorsolateral prefrontal cortex (dlPFC), the dorsomedial prefrontal cortex (dmPFC), and the ventromedial prefrontal cortex (vmPFC). Machine learning methods were used to predict the participants' perceptions of leaders based on their brain activity. The study has identified the brain regions that are involved in processing political stimuli and making judgments about political leaders. Within this study, the best-performing machine learning model, LightGBM, achieved a highest accuracy score of 0.78, underscoring its efficacy in predicting voters' perceptions of political leaders based on the brain activity of the former. The findings from this study provide new insights into the neural basis of political decision-making and the development of effective political marketing campaigns while bridging neuromarketing, political science and machine learning, in turn enabling predictive insights into voter preferences and behaviorWOS:0011358179000012-s2.0-8518158251138188505Science Citation Index ExpandedArticleUluslararası işbirliği ile yapılmayan - HAYIROcak2024YÖK - 2023-24Kası