Advanced Computational and Linguistic Analytics
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 January 2023) | Viewed by 12354
Special Issue Editors
Interests: open data; digital and electronic government; data-driven innovation; health informatics cities and regions
Special Issue Information
Dear Colleagues,
All of you are welcomed to submit your original, innovative, and state-of-the-art research to the Special Issue entitled "Advanced Computational and Linguistic Analytics”.
Big Data analytics is currently fuelling digital transformation in society by offering solutions based on knowledge about hidden patterns, relationships, and other powerful insights resulting from employing complex data-driven computational and linguistic analytics methods (Iqbal, Doctor, More, Mahmud, and Yousuf, 2020). Traditional analytics methods which include multivariate and multivariate analysis, predictive and explanatory models, association analysis, classification or clustering, machine learning, etc. are employed for structured data. With advances in hardware and software technologies such as social networking, the Internet of Things, wearable sensors, mobile technology, storage, and cloud computing, multiple sources of Big Data are increasingly unstructured or, at best, semi-structured (Baars and Kemper, 2008). Thus, it has become necessary to (i) develop new advanced computational techniques for processing large unstructured (textual) datasets and (ii) use the power of linguistic-based text analytics to enable the extraction of deep insights in complementing the analysis of related quantitative or structured data. The potential use of computational and linguistic analytics opens up new opportunities in a wide variety of application contexts. Typical linguistic analytics techniques include content analysis, entity extraction, text classification and clustering, topic modelling, and sentiment and opinion analysis using algorithms based on machine learning and natural language processing.
Despite the promise and potential of textual data and linguistic analytics techniques, their widespread use is still limited (Ojo and Rizun, 2021a). One of the barriers is the cost and resources required to meaningfully process the text beyond the automated text analysis commonly employed in practice in order to produce the deep insights characteristic of traditional qualitative analysis at scale (Maramba et al., 2015). Besides, the adoption and use of computational and linguistic analytics techniques are very often constrained by the need for providers and policymakers to base their decisions and actions on sound statistical measures not supported in traditional text analytics techniques (Ojo and Rizun, 2021b). In addition, studies involving the use of computational techniques and methods related to Big Data analytics in IS research have also been criticised for weak theoretical contributions (Kar and Dwivedi, 2020).
This Special Issue aims to expand the understanding of the role of advanced computational and linguistic analytics in a variety of application contexts. We invite researchers to contribute original, innovative, and state-of-the-art research articles, as well as review articles. We particularly encourage contributions on novel methodological approaches, such as those adapting computational techniques traditionally associated with quantitative data for processing textual data or building substantive theories inductively from large textual data.
Potential topics include, but are not limited to, the role of advanced computational and linguistic analytics in the following application contexts:
- Service improvement and transformation;
- Business processes improvement;
- Data-driven decision-making and policy-making support;
- e-Services customer supervision;
- Policy analytics for electronic government;
- Open government data utilization;
- Smart sustainable cities and urban analytics;
- Digital society and online participation informational support;
- Enabling strategic alignment and organisational transformation.
Dr. Adegboyega Ojo
Dr. Rizun Nina
Guest Editors
Manuscript Submission Information
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Keywords
- computational analytics
- linguistic analytics
- big data
- data mining
- text mining
- NLP
- artificial intelligence
- machine learning
- deep learning
- health care
- digital media data
- open government data
- smart sustainable cities
- urban analytics
- data-driven
- policymaking
- decision making
- business process
- service quality
- strategic alignment
- disruptive technologies
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