scholarly journals Opinion Mining and Sentiment Analysis in Marketing Communications: A Science Mapping Analysis in Web of Science (1998–2018)

2020 ◽  
Vol 9 (3) ◽  
pp. 23 ◽  
Author(s):  
Pablo Sánchez-Núñez ◽  
Carlos de las Heras-Pedrosa ◽  
José Ignacio Peláez

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.

Land ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 488
Author(s):  
Mercedes Jiménez-García ◽  
José Ruiz-Chico ◽  
Antonio Rafael Peña-Sánchez

Tourism and landscape are broad and complex scientific research fields, as is the synergy between them has given rise to a volume of articles diverse in nature, subject matter and methodology. These difficulties mean that, at present, there is no complete theoretical framework to support this tourism and landscape research, nor complete knowledge of its structure and organization. This motivates the present work, which constitutes the first attempt at mapping this research topic by applying bibliometric techniques using VOSviewer and Science Mapping Analysis Software Tool (SciMAT) software. A total of 3340 articles from journals indexed in Web of Science were analyzed. The results obtained confirm that interest in the study of these concepts has been growing, especially in the last decade. The main contribution of this work lies in the identification of work themes that were basic to the construction of the field but that are currently in decline, such as “cultural heritage” and other themes important to the field that should continue to be dealt with, such as “national parks” or “geotourism”. The transversal nature of sustainability that appears in the network of keywords related to currently emerging themes, such as “planning” and “environment”, is also highlighted and reinforced.


F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1210
Author(s):  
Juan Rincon-Patino ◽  
Gustavo Ramirez-Gonzalez ◽  
Juan Carlos Corrales

Background: Machine learning is becoming increasingly important for companies and the scientific community. In this study, we perform a bibliometric analysis on machine learning research, in order to provide an overview of the scientific work during the period 2007-2017 in this area and to show trends that could be the basis for future developments in the field. Methods: This study is carried out using the SciMAT tool based on results extracted from Scopus. This analysis shows the strategic diagrams of evolution and a set of thematic networks. The results provide information on broad tendencies of machine learning. Results: The results show that SciMAT is a useful tool to carry out a science mapping analysis, and emphasizes the premise that machine learning has boundless applications and will continue to be an interesting research field in the future. Conclusions: Some of the conclusions exposed in this study show that classification algorithms have been widely studied and represent a relevant tool for generating different machine learning applications. Nonetheless, regression algorithms are becoming increasingly important in the scientific community, allowing the generation of solutions to predict diseases, sales, and yields, for example.


Author(s):  
Carina Soledad González González

Este estudio revisa la literatura científica sobre el uso de tecnologías tangibles en la educación infantil, a fin de: a) identificar qué tecnologías tangibles se han utilizado; b) reconocer los objetivos educativos de la utilización de estas tecnologías y c) presentar una síntesis de la evidencia empírica disponible sobre su efectividad educativa. La búsqueda sistemática fue realizada en la base de datos “Web of Science (WoS)” y se analizaron utilizando la herramienta de software científico “Science Mapping Analysis”. Luego, se incluyeron 29 documentos relevantes de los últimos cinco años en el estudio de revisión. Para cada artículo, se analizó el propósito del estudio, el tipo de tecnología tangible utilizada, el método de investigación aplicado, las características de la muestra y los principales resultados obtenidos. Los artículos revisados sugieren que la principal tecnología tangible utilizada en la educación infantil es la tableta digital y la alfabetización (básica y emergente) es el área más estudiada, y con resultados prometedores.


Author(s):  
Antonio-José Moreno-Guerrero ◽  
María Elena Parra-González ◽  
Jesús López-Belmonte ◽  
Adrián Segura-Robles

Author(s):  
Beatrice I. J. M. Van der Heijden ◽  
Karen Pak ◽  
Mónica Santana

This paper provides a systematic review of the phenomenon of menopause at the workplace from a sustainable career perspective, by highlighting its major themes along with the evolution and tendencies observed in this field. A conceptual science mapping analysis based on co-word bibliographic networks was developed, using the SciMAT tool. From 1992 to 2020, 185 documents were retrieved from the Web of Science. In the first analyzed time span (1992–2002), postmenopausal women, health, and risk factors appeared to be the motor themes (well-developed and important for the structure of the discipline under focus), and disorder was an emerging or disappearing theme in the phenomenon under research. In the second studied period (2003–2013), risk and health were motor themes, menopausal symptoms was a basic or transversal theme (important for the discipline but not well-developed), coronary heart disease was a specialized theme (well-developed but less important for the structure of the research field), and postmenopausal women was an emerging or disappearing theme (both weakly developed and marginal to the field). In the third studied period (2014–2020), menopause, breast cancer, and menopausal symptoms were motor themes, Anxiety was a specialized theme and risk and body mass index were emerging or disappearing themes. Sustainability of women’s careers in the second half of life is of increasing importance given the increasing equal representation of men and women in working organizations, and the impact of the changing nature of work in the 21st century on older workers.


F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1240 ◽  
Author(s):  
Juan Rincon-Patino ◽  
Gustavo Ramirez-Gonzalez ◽  
Juan Carlos Corrales

Background: Machine learning researches algorithms that allow a machine to learn about resolving problems in different application domains. Due to the wide number of machine learning applications, it is necessary for newcomers to the field to have alternatives to explore this field faster. Methods: In this paper, we present a science mapping analysis on the machine learning research in the period 2007-2017. This study was develop using the CiteSpace tool based on results from Clarivate Web of Science. This analysis shows how the field has evolved, by highlighting the most notable authors, institutions, keywords, countries, categories, and journals. Results: The results provide information on trends and possibilities in the near future, particularly in areas such as health, biology and banking, where machine learning is a valuable tool to generate solutions. Conclusions: Machine learning is being widely studied, and several institutions in countries like the USA and China constantly generate machine learning based solutions. Diseases, such as cancer or Alzheimer’s disease, studies in biology, such as the protein molecule, virtual reality, commerce, smartphones, and ubiquitous computing, are all fields where machine learning contributes to resolving problems.


2019 ◽  
Author(s):  
José-Ricardo López-Robles ◽  
Javier Guallar ◽  
José-Ramón Otegi-Olaso ◽  
Nadia-Karina Gamboa-Rosales

The current research conducts a bibliometric performance and intellectual structure analysis of El profesional de la información(EPI) from 2006 to 2017. On the one hand, the EPI’s performance is analyzed according to the data retrieved from the database Social Sciences Citation Index (SSCI), part of Web of Science Core Collection, putting the focus on the productivity of the authors, number of references, organizations, countries and main publications. On the other hand, the intellectual structure of the journal is analyzed with SciMAT, an open source (GPLv3) bibliometric software tool developed to perform a science mapping analysis under a longitudinal framework, identifying the main thematic areas that have been the object of research, their composition, relationship and evolution during the period analyzed.


2021 ◽  
Vol 42 (02) ◽  
pp. 203-221
Author(s):  
Iria PAZ-GIL ◽  
◽  
Jessica PAULE-VIANEZ ◽  
Alberto PRADO-ROMAN ◽  
◽  
...  

This study contributes to the existing literature by providing a current picture of research on blood donation behavior. The methodology applied is a bibliometric analysis, based on scientific production and the science mapping analysis. The analysis is applied over a sample of 963 articles published between 1957 and 2017, in the Web of Science (WoS). Most active journals, authors, countries are identified as well as the main topics in this research area.


Comunicar ◽  
2018 ◽  
Vol 26 (55) ◽  
pp. 81-91 ◽  
Author(s):  
Julio Montero-Díaz ◽  
Manuel-Jesús Cobo ◽  
María Gutiérrez-Salcedo ◽  
Francisco Segado-Boj ◽  
Enrique Herrera-Viedma

Communication Research field has an extraordinary growth pattern, indeed bigger than other research fields. In order to extract knowledge from such amount, intelligent techniques are needed. In such a way, using bibliometric techniques, the evolution of the conceptual, social and intellectual aspects of this research field could be analysed, and hence, understood. Although the communication research field has been widely analysed using bibliometric techniques and science mapping tools, a conceptual analysis of the whole communication research field is still needed. Therefore, this article introduces the first science mapping analysis in the communication research field based on the Web of Science Subject Category "Communication," showing its conceptual structure and scientific evolution. SciMAT, a bibliometric science mapping software tool based on co-word analysis and h-index, is applied using a sample of 33.627 research documents from 1980 to 2013 published in 74 main communication journals indexed in the Journal Citation Reports of the Web of Science. The results show that research conducted in the communication research is concentrated on the following sixteen disconnected thematic areas: “children”, “psychological aspects”, “news”, “audience”, “surveys”, “advertising”, “health”, “relationship”, “gender”, “discourse”, “telephone communication”, “public relation”, “telecommunications”, “public opinion”, “activism” and “internet”. These areas have progressively disconnected among them, which drives to a Communication field relatively fragmented. El campo científico de la comunicación ha experimentado un enorme crecimiento a lo largo de los años, superando incluso a algunas áreas científicas consagradas. Mediante el uso de técnicas bibliométricas, podemos analizar la evolución conceptual, social e intelectual de esta área, así como comprenderla. En particular, el área de «Comunicación» ha sido ampliamente estudiada desde un punto de vista bibliométrico, pero no se ha realizado un análisis conceptual global del área englobado en un marco longitudinal. En este sentido, este artículo muestra el primer análisis de mapas científicos del área de investigación de la comunicación basándose en la Categoría de la Web of Science «Communication», centrándose en la estructura conceptual y cómo esta ha evolucionado. El estudio se ha realizado mediante la herramienta de análisis de mapas científicos SciMAT, basada en los mapas de co-palabras y en el índice-h. Un conjunto de 33.627 artículos científicos, publicados entre 1980 y 2013 en las 74 principales revistas del Journal Citation Reports de la Web of Science, han sido estudiados. Analizando los resultados, podemos destacar que la investigación llevada a cabo en el área de la comunicación se ha centrado en dieciséis áreas temáticas: «infancia», «aspectos psicológicos», «noticias», «audiencias», «sondeos», «publicidad», «salud», «relaciones», «género», «discurso», «comunicación telefónica», «relaciones públicas», «telecomunicaciones», «opinión pública», «activismo» e «Internet». Estas áreas se han desconectado entre ellas progresivamente, lo que conduce a un campo relativamente fragmentado.


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