8,347 research outputs found
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
Computational Models (of Narrative) for Literary Studies
In the last decades a growing body of literature in Artificial Intelligence (AI) and Cognitive
Science (CS) has approached the problem of narrative understanding by means of computational
systems. Narrative, in fact, is an ubiquitous element in our everyday activity and
the ability to generate and understand stories, and their structures, is a crucial cue of our intelligence.
However, despite the fact that - from an historical standpoint - narrative (and narrative
structures) have been an important topic of investigation in both these areas, a more
comprehensive approach coupling them with narratology, digital humanities and literary
studies was still lacking.
With the aim of covering this empty space, in the last years, a multidisciplinary effort
has been made in order to create an international meeting open to computer scientist, psychologists,
digital humanists, linguists, narratologists etc.. This event has been named CMN
(for Computational Models of Narrative) and was launched in the 2009 by the MIT scholars
Mark A. Finlayson and Patrick H. Winston1
How to Find Opinion Leader on the Online Social Network?
Online social networks (OSNs) provide a platform for individuals to share
information, exchange ideas and build social connections beyond in-person
interactions. For a specific topic or community, opinion leaders are
individuals who have a significant influence on others' opinions. Detecting and
modeling opinion leaders is crucial as they play a vital role in shaping public
opinion and driving online conversations. Existing research have extensively
explored various methods for detecting opinion leaders, but there is a lack of
consensus between definitions and methods. It is important to note that the
term "important node" in graph theory does not necessarily align with the
concept of "opinion leader" in social psychology. This paper aims to address
this issue by introducing the methodologies for identifying influential nodes
in OSNs and providing a corresponding definition of opinion leaders in relation
to social psychology. The key novelty is to review connections and
cross-compare different approaches that have origins in: graph theory, natural
language processing, social psychology, control theory, and graph sampling. We
discuss how they tell a different technical tale of influence and also propose
how some of the approaches can be combined via networked dynamical systems
modeling. A case study is performed on Twitter data to compare the performance
of different methodologies discussed. The primary objective of this work is to
elucidate the progression of opinion leader detection on OSNs and inspire
further research in understanding the dynamics of opinion evolution within the
field
A case study of predicting banking customers behaviour by using data mining
Data Mining (DM) is a technique that examines information stored in large database or data warehouse and find the patterns or trends in the data that are not yet known or suspected. DM techniques have been applied to a variety of different domains including Customer Relationship Management CRM). In this research, a new Customer Knowledge Management (CKM) framework based on data mining is proposed. The proposed data mining framework in this study manages relationships between banking organizations and their customers. Two typical data mining techniques - Neural Network and Association Rules - are applied to predict the behavior of customers and to increase the decision-making processes for recalling valued customers in banking industries. The experiments on the real world dataset are conducted and the different metrics are used to evaluate the performances of the two data mining models. The results indicate that the Neural Network model achieves better accuracy but takes longer time to train the model
A survey of data mining techniques for social media analysis
Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors
USER-GENERATED CONTENT (UGC) ENCOUNTERED ENTERPRISE-GENERATED CONTENT (EGC): QUANTIFYING THE IMPACT OF EGC ON THE PROPAGATION OF NEGATIVE UGC
The impact of user-generated content (UGC), especially negative UGC on enterprises is well recognized. From the perspective of enterprises, different strategies of enterprise-generated content (EGC) have also been adapted to response to the unexpected UGC, but few studies have investigated the influence of such strategies on the UGC propagation. This research examines which strategy on the negative UGC propagation is optimal by proposing EGC-UGC interaction model. It aims to understand the interaction between UGC and EGC in the context of the social network. Using a simulation analysis method to measure the effect of such EGC factors as the first time of issuing EGC, EGC quantity and interactive frequency on the UGC propagation, the study finds that interactive frequency is the most key factor in defending against negative UGC propagation. This research further explores the effect of different strategy combination referring those three factors on the two types of negative UGC propagation based on deviation distance. The results present two optimal strategies for the two types of negative UGC propagation, respectively. Overall, these findings offer some unique implication for UGC management, information diffusion model of competitive information coexisting
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