Twitter Ontology-Driven Sentiment Analysis

Cotfas, Liviu‐Adrian; Delcea, Camelia; Roxin, Ioan; Paun, Ramona

doi:10.1007/978-3-319-16211-9_14

Cited by 15 publications

(5 citation statements)

References 12 publications

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“…As is reported in the literature (Corchado et al, 2009;Gabrys et al, 2005;Grosan and Abraham, 2011;Intan, 2015;Karray and De Silva, 2004;Nguyen and Szczerbitscky, 2009;Pedrycz and Chen, 2011;Sinha and Gupta, 2000;Delcea, 2015;Delcea et al, (2014a, b, c, d); Cotfas et al, 2015;Bradea et al, 2014a, b), several techniques and heuristics have been used to improve the general efficiency of the hybrid architectures. Some of most used hybrid architectures are summarized as follows:…”

Section: The Hybrid Architectures With Grey Systems Modelsmentioning

confidence: 58%

The hybrid intelligent systems design using grey systems theory

Scarlat

Maracine

2015

Grey Systems: Theory and Application

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Purpose – The purpose of this paper is to discuss how tacit and explicit knowledge determine grey knowledge and how these are stimulated through interactions within networks, forming the grey hybrid intelligent systems (HISs). The feedback processes and mechanisms between internal and external knowledge determine the apparition of grey knowledge into an intelligent system (IS). The extension of ISs is determined by the ubiquity of the internet but, in our framework, the grey knowledge flows assure the viability and effectiveness of these systems. Design/methodology/approach – Some characteristics of the Hybrid Intelligent Knowledge Systems are put forward along with a series of models of hybrid computational intelligence architectures. More, relevant examples from the literature related to the hybrid systems architectures are presented, underlying their main advantages and disadvantages. Findings – Due to the lack of a common framework it remains often difficult to compare the various HISs conceptually and evaluate their performance comparatively. Different applications in different areas are needed for establishing the best combinations between models that are designed using grey, fuzzy, neural network, genetic, evolutionist and other methods. But all these systems are knowledge dependent, the main flow that is used in all parts of every kind of system being the knowledge. Grey knowledge is an important part of the real systems and the study of its proprieties using the methods and techniques of grey system theory remains an important direction of the researches. Originality/value – The paper discusses the differences among the three types of knowledge and how they and the grey systems theory can be used in different hybrid architectures.

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Section: The Hybrid Architectures With Grey Systems Modelsmentioning

confidence: 58%

The hybrid intelligent systems design using grey systems theory

Scarlat

Maracine

2015

Grey Systems: Theory and Application

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“…Currently, possible developed semantic ontologies that could be adapted include Financial Industry Business Ontology FIBO [17,33] and Suggested Upper Merged Ontology (SUMO) [34] in finance domain, and Friend-of-a-Friend (FOAF) and Semantically Interlinked Online Communities (SIOC) in online networking domain [35,6].…”

Section: Positioning Of the Researchmentioning

confidence: 99%

“…Various approaches and data sources are used trying to achieve better results and/or tools solving the issues of complex formats, structures and meanings. Yet, the majority of the works used limited and basic semantic ontologies and did not fully imply the Big Data for example [35,40]. Currently, the enhancement of Big Data using ontologies and semantic techniques to solve the variety challenge is at the early stages and still a research challenge that requires further work especially in the business and financial domain, which creates an opportunity to have a good contribution to the knowledge.…”

Section: Positioning Of the Researchmentioning

confidence: 99%

Creating Intelligent Business Systems by Utilising Big Data and Semantics

Quboa

Mehandjiev

2017

2017 IEEE 19th Conference on Business Informatics (CBI)

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Increasing volumes of data are generated from a variety of industries, organizations and people. Such data is now seen as a key business asset, since analysing it allows business decision makers to make choices with confidence. This data is often "big data"-a data of such volume, velocity and variety that conventional processing techniques are not suitable. We research the specific challenge of variety, where data comprises a number of incompatible formats. Semantic technology, or the use of ontologies, is seen as a core approach to resolving this challenge and aligning the data generated from heterogeneous data sources. This paper lays down the issues which should be addressed and reviews the work which has been done in the area of integrating structured and unstructured data sources, with a special focus on the financial domain. We then chart a research plan in the area following Design Science research.

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“…Making a short analysis, it can be seen that only in 2015, there were recorded a number of 64672 papers written in the social media area, 2687 focusing mainly on the online social networks. (Delcea et al, 2014a) The main focus areas were: social issues such as: confidentiality and privacy (Lewis et al, 2008;Nosko et al, 2010), age gap (Pfeil et al, 2009), social activity (Cheung et al, 2011;Cheung and Lee, 2010), sentiment analysis (Cotfas et al, 2015(Cotfas et al, , 2016, social assets (Ellison et al, 2007), communication (Subrahmanyam et al, 2008), addiction (Benevenuto et al, 2009); elearning (Cotfas, 2011;Cotfas and Roxin, 2013;Greenhow and Robelia, 2009); risk within companies (Schniederjans et al, 2013); e-commerce and online public goods (Wasko et al, 2009); health issues (Guseh et al, 2009;Thompson et al, 2008)and patient-doctor relationship in social media (Bosslet et al, 2011). As for the main research areas of the papers published on Web-of-Science (WoS, 2016) see Considering just the online social network research, in Fig.…”

Section: Online Social Network and Social Media Engagement Researchmentioning

confidence: 99%

Grey social media engagement analysis

Delcea

2016

Grey Systems: Theory and Application

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Twitter Ontology-Driven Sentiment Analysis

Cited by 15 publications

References 12 publications

The hybrid intelligent systems design using grey systems theory

The hybrid intelligent systems design using grey systems theory

Creating Intelligent Business Systems by Utilising Big Data and Semantics

Grey social media engagement analysis

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