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

Belief-based action prediction in preverbal infants

Successful mindreading entails both the ability to think about what others know or believe, and to use this knowledge to generate predictions about how mental states will influence behavior. While previous studies have demonstrated that young infants are sensitive to others’ mental states, there continues to be much debate concerning how to characterize early theory of mind abilities. In the current study, we asked whether 6-month-old infants appreciate the causal role that beliefs play in action. Specifically, we tested whether infants generate action predictions that are appropriate given an agent’s current belief. We exploited a novel, neural indication of action prediction: motor cortex activation as measured by sensorimotor alpha suppression, to ask whether infants would generate differential predictions depending on an agent’s belief. After first verifying our paradigm and measure with a group of adult participants, we found that when an agent had a false belief that a ball was in the box, motor activity indicated that infants predicted she would reach for the box, but when the agent had a false belief that a ball was not in the box, infants did not predict that she would act. In both cases, infants based their predictions on what the agent, rather than the infant, believed to be the case, suggesting that by 6 months of age, infants can exploit their sensitivity to other minds for action prediction

Multimodal Polynomial Fusion for Detecting Driver Distraction

Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone. Although there has been a considerable amount of research on modeling the distracted behavior of drivers under various conditions, accurate automatic detection using multiple modalities and especially the contribution of using the speech modality to improve accuracy has received little attention. This paper introduces a new multimodal dataset for distracted driving behavior and discusses automatic distraction detection using features from three modalities: facial expression, speech and car signals. Detailed multimodal feature analysis shows that adding more modalities monotonically increases the predictive accuracy of the model. Finally, a simple and effective multimodal fusion technique using a polynomial fusion layer shows superior distraction detection results compared to the baseline SVM and neural network models.Comment: INTERSPEECH 201

Readers’ cognitive processes during IELTS reading tests: evidence from eye tracking

The research described in this report investigates readers' mental processes as they complete onscreen IELTS (International English Language Testing System) reading test items. It employs up-to-date eye tracking technology to research readers' eye movements and aims, among other things, to contribute to an understanding of the cognitive validity of reading test items (Glaser. 1991; Field forthcoming). Participants were a group of Malaysian undergraduates (n=71) taking an onscreen test consisting of two IELTS reading passages with a total of 11 test items. The eye movements of a random sample of these participants (n=38) were tracked. Questionnaire and stimulated recall interview data were also collected, and were important in order to interpret and explain the eye tracking data. Findings demonstrated significant differences between successful and unsuccessful test-takers on a number of dimensions, including their ability to read expeditiously (Khalifa and Weir. 2009). and their focus on particular aspects of the test items and the reading texts. This demonstrates the potential of eye tracking, in combination with post- hoc interview and questionnaire data, to offer new insights into the cognitive processes of successful and unsuccessful candidates in a reading test. It also gives unprecedented insights into the cognitive processing of successful and unsuccessful readers doing language tests. As a consequence, the findings should be of value to teachers and learners, and also to examination boards seeking to validate and prepare reading tests, as well as psycholinguists and others interested in the cognitive processes of readers