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1 |
Text mining of industry 4:0 job advertisements
استخراج متن آگهی های شغلی صنعت 4:0-2020 Since changes in job characteristics in areas such as Industry 4.0 are rapid, fast tool for analysis of job advertisements
is needed. Current knowledge about competencies required in Industry 4.0 is scarce. The goal of this
paper is to develop a profile of Industry 4.0 job advertisements, using text mining on publicly available job
advertisements, which are often used as a channel for collecting relevant information about the required
knowledge and skills in rapid-changing industries. We searched website, which publishes job advertisements,
related to Industry 4.0, and performed text mining analysis on the data collected from those job advertisements.
Analysis of the job advertisements revealed that most of them were for full time entry; associate and mid-senior
level management positions and mainly came from the United States and Germany. Text mining analysis resulted
in two groups of job profiles. The first group of job profiles was focused solely on the knowledge related to
Industry 4.0: cyberphysical systems and the Internet of things for robotized production; and smart production
design and production control. The second group of job profiles was focused on more general knowledge areas,
which are adapted to Industry 4.0: supply change management, customer satisfaction, and enterprise software.
Topic mining was conducted on the extracted phrases generating various multidisciplinary job profiles. Higher
educational institutions, human resources professionals, as well as experts that are already employed or aspire to
be employed in Industry 4.0 organizations, would benefit from the results of our analysis. Keywords: Human resource management | Text mining | Job profiles | Big data analytics | Industry 4.0 | Education | Smart factory |
مقاله انگلیسی |
2 |
Emotional Text Mining: Customer profiling in brand management
متن کاوی عاطفی: نمایه سازی مشتری در مدیریت برند-2020 The widespread use of the Internet and the constant increase in users of social media platforms has made a large amount of textual data available. This represents a valuable source of information about the changes in people’s opinions and feelings. This paper presents the application of Emotional Text Mining (ETM) in the field of brand management. ETM is an unsupervised procedure aiming to profile social media users. It is based on a bottom-up approach to classify unstructured data for the identification of social media users’ representations and sentiments about a topic. It is a fast and simple procedure to extract meaningful information from a large collection of texts. As customer profiling is relevant for brand management, we illustrate a business application of ETM on Twitter messages concerning a well-known sportswear brand in order to show the potential of this procedure, high- lighting the characteristics of Twitter user communities in terms of product preferences, representations, and sentiments. Keywords: Emotional Text Mining | Brand management | Twitter | Network analysis | Customer profiling |
مقاله انگلیسی |
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Business analytics for strategic management: Identifying and assessing corporate challenges via topic modeling
تجزیه و تحلیل کسب و کار برای مدیریت استراتژیک: شناسایی و ارزیابی چالش های شرکت از طریق مدل سازی موضوع-2020 Strategic management requires an assessment of a firms internal and external environments. Our work extends the body of management tools (e.g., SWOT analysis or growth-share matrix) by proposing an automated text mining framework. Here we draw on narrative materials from firms (e.g., financial disclosures) and perform topic modeling so as to identify the key issues faced by an organization. We then quantify the use of language along two dimensions: risk and optimism. This reveals a firms strengths and weaknesses by identifying business units, activities, and processes subject to risk, while also comparing it with competitors or the market. Keywords: Business analytics | Text mining | Firm performance | Topic modeling | Latent Dirichlet allocation | Strategic management |
مقاله انگلیسی |
4 |
What can the news tell us about the environmental performance of tourist areas? A text mining approach to China’s National 5A Tourist Areas
چه خبرهایی می تواند از عملکرد زیست محیطی مناطق توریستی به ما بگوید؟ یک رویکرد استخراج متن به مناطق گردشگری ملی 5A در چین-2020 This study aims to evaluate the environmental performance status of tourist areas and explore the influencing
factors using text mining of web news. As the leading tourist attractions in China, the National 5A Tourist Areas
face severe environmental challenges, and were hence chosen to exemplify the rapid assessment approach in the
big data era. This study used over 1,300,000 words from online news sources and assessed the environmental
performance of 120 National 5A Tourist Areas to conclude that (1) water is the most impacted environmental
resource; (2) tourist area environmental performance can be classified into (a) environmental pollution, (b)
ecological and resource pressure, (c) landscape character issues and (d) others; and (3) the primary factors
influencing the environment are tourism and business operating activities, with the tourist areas’ environmental
performance types being strongly related to their spatial locations and weakly related to their resource types. By
comparing the environmental performance types in this paper with related research the effectiveness of this
study’s approach is validated. These conclusions and this approach can provide guidelines and tools for environmental
assessment and promote tourist area management Keywords: Environmental impact assessment | Environmental pollution | Tourist environment | Web content | Text mining | China’s National 5A Tourist Areas |
مقاله انگلیسی |
5 |
The Diffusion Barriers of AI Mobility Service: the Case of TADA
موانع انتشار خدمات تحرک هوش مصنوعی: مورد TADA-2020 TADA, AI-based smart mobility service in Korea, has
been indicted for violating the act in Korea despite the
uncertainty of whether it is illegal or not. The main reason for
this is that TADA has not found a way to coexist with traditional
mobility companies. The TADA case shows that the diffusion of
innovation is affected by a variety of social issues, not just
technical or business issues. Thus, this study analyzes public
perceptions of the TADA case and discusses what are the social
barriers to AI innovation. The results of this study show that the
diffusion of innovative services requires discussions with various
stakeholders in addition to technical or business efforts. Keywords: mobility service | diffusion | barrier | Text-mining | TADA |
مقاله انگلیسی |
6 |
Data mining for smart legal systems
داده کاوی برای سیستمهای حقوقی هوشمند-2019 Smart legal systems carry immense potential to provide legal community and public with valuable insights using legal data. These systems can consequently help in analyzing and mitigating various social issues. In Pakistan, since last couple of years, courts have been reporting judgments online for public consumption. This public data, once processed, can be utilized for betterment of society and policy making in Pakistan. This study takes the first step to realize smart legal system by extracting various entities such as dates, case numbers, reference cases, person names, etc. from legal judgments. To automatically ex- tract these entities, the primary requirement is to construct dataset using legal judgments. Hence, firstly annotation guidelines are prepared followed by preparation of annotated dataset for extraction of various legal entities. Experiments conducted using variety of datasets, multiple algorithms and annotation schemes, resulted into maximum F1-score of 91.51% using Conditional Random Fields Keywords: Information extraction | Named Entity Recognition | Legal data | Text mining | Civil law proceeding |
مقاله انگلیسی |
7 |
Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer
بررسی عوارض جانبی دارویی در خلاصه تخلیه سوابق پزشکی الکترونیکی با استفاده از Readpeer-2019 Background: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts
to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through
all discharge summaries to find cases of drug-adverse event (AE) relations.
Purpose: The objective of this paper is to develop a natural language processing (NLP) framework to detect drug-
AE relations from unstructured hospital discharge summaries.
Basic procedures: An NLP algorithm was designed using customized dictionaries of drugs, adverse event (AE)
terms, and rules based on trigger phrases, negations, fuzzy logic and word distances to recognize drug, AE terms
and to detect drug-AE relations. Furthermore, a customized annotation tool was developed to facilitate expert
review of discharge summaries from a tertiary hospital in Singapore in 2011.
Main findings: A total of 33 trial sets with 50 to 100 records per set were evaluated (1620 discharge summaries)
by our algorithm and reviewed by pharmacovigilance experts. After every 6 trial sets, drug and AE dictionaries
were updated, and rules were modified to improve the system. Excellent performance was achieved for drug and
AE entity recognition with over 92% precision and recall. On the final 6 sets of discharge summaries (600
records), our algorithm achieved 75% precision and 59% recall for identification of valid drug-AE relations.
Principal conclusions: Adverse drug reactions are a significant contributor to health care costs and utilization.
Our algorithm is not restricted to particular drugs, drug classes or specific medical specialties, which is an
important attribute for a national regulatory authority to carry out comprehensive safety monitoring of drug
products. Drug and AE dictionaries may be updated periodically to ensure that the tool remains relevant for
performing surveillance activities. The development of the algorithm, and the ease of reviewing and correcting
the results of the algorithm as part of an iterative machine learning process, is an important step towards use of
hospital discharge summaries for an active pharmacovigilance program Keywords: Pharmacovigilance | Text mining | Electronic medical records | Expert system | Adverse drug reaction |
مقاله انگلیسی |
8 |
Validation of text-mining and content analysis techniques using data collected from veterinary practice management software systems in the UK
اعتبارسنجی تکنیک های استخراج متن و تجزیه و تحلیل محتوا با استفاده از داده های جمع آوری شده از سیستم های نرم افزار مدیریت دامپزشکی در انگلستان-2019 Electronic patient records from practice management software systems have been used extensively in medicine
for the investigation of clinical problems leading to the creation of decision support frameworks. To date,
technologies that have been utilised for this purpose such as text mining and content analysis have not been
employed significantly in veterinary medicine.
The aim of this research was to pilot the use of content analysis and text-mining software for the synthesis and
analysis of information extracted from veterinary electronic patient records. The purpose of the work was to be
able to validate this approach for future employment across a number of practices for the purposes of practice
based research. The approach utilised content analysis (Prosuite) and text mining (WordStat) software to aggregate
the extracted text. Text mining tools such as Keyword in Context (KWIC) and Keyword Retrieval (KR)
were employed to identify specific occurrences of data across the records. Two different datasets were interrogated,
a bespoke test dataset that had been set up specifically for the purpose of the research, and a functioning
veterinary clinic dataset that had been extracted from one veterinary practice.
Across both datasets, the KWIC analysis was found to have a high level of accuracy with the search resulting
in a sensitivity of between 85.3–100%, a specificity of between 99.1–99.7%, a positive predictive value between
93.5–95.8% and a negative predictive value between 97.7–100%. The KR search, based on machine learning,
was utilised for the clinic-based dataset and was found to perform slightly better than the KWIC analysis.
This study is the first to demonstrate the application of content analysis and text mining software for validation
purposes across a number of different datasets for the purpose of search and recall of specific information
across electronic patient records. This has not been demonstrated previously for small animal veterinary epidemiological
research for the purposes of large scale analysis for practice-based research. Extension of this work
to investigate more complex diseases across larger populations is required to fully explore the use of this approach
in veterinary practice. Keywords: Text mining | Content analysis | Veterinary practice | Practice based research |
مقاله انگلیسی |
9 |
Using big data database to construct new GFuzzy text mining and decision algorithm for targeting and classifying customers
استفاده از بانک اطلاعاتی داده های بزرگ برای ساخت الگوریتم تصمیم گیری متن کاوی GFuzzy برای هدف قرار دادن و طبقه بندی مشتریان-2019 After an enterprise builds a data warehouse, it can record information related to customer interactions using
structured and unstructured data. The intention is to convert these data into useful information for decisionmaking
to ensure business continuity. Hence, this study proposes a new Chinese text classification model for the
project management office (PMO) using fuzzy semantics and text mining techniques. First, content analysis is
performed on the unstructured data to convert important textual information and compile it into a keyword
index. Next, a classification and decision algorithm for grey situations and fuzzy (GFuzzy) is used to categorize
textual data by three characteristics: maximum impact, moderate impact, and minimum impact. The purpose is
to analyze consumer behaviors for the accurate classification of customers. Lastly, a more effective marketing
strategy is formulated to target the various customer combinations, growth models, and the best mode of service.
A company database of interactions with customers is used to construct a text mining model and to analyze the
decision process of its PMO. The purpose is to test the feasibility and validity of the proposed model so that
enterprises are provided with better marketing strategies and PMO processes aimed at their customers Keywords: Big data warehouse | Content analysis | Data mining | Fuzzy grey situation decision-making algorithm | Project management office | Customer relations management |
مقاله انگلیسی |
10 |
A decision support system based on ontology and data mining to improve design using warranty data
یک سیستم پشتیبانی تصمیم گیری مبتنی بر هستی شناسی و داده کاوی برای بهبود طراحی با استفاده از گارانتی داده ها -2019 Analysis of warranty based big data has gained considerable attention due to its potential for improving the
quality of products whilst minimizing warranty costs. Similarly, customer feedback information and warranty
claims, which are commonly stored in warranty databases might be analyzed to improve quality and reliability
and reduce costs in areas, including product development processes, advanced product design, and manufacturing.
However, three challenges exist, firstly to accurately identify manufacturing faults from these multiple
sources of heterogeneous textual data. Secondly, accurately mapping the identified manufacturing faults with
the appropriate design information and thirdly, using these mappings to simultaneously optimize costs, design
parameters and tolerances. This paper proposes a Decision Support System (DSS) based on novel integrated
stepwise methodologies including ontology-based text mining, self-organizing maps, reliability and cost optimization
for identifying manufacturing faults, mapping them to design information and finally optimizing design
parameters for maximum reliability and minimum cost respectively. The DSS analyses warranty databases which
collect the warranty failure information from the customers in a textual format. To extract the hidden knowledge
from this, an ontology-based text mining based approach is adopted. A data mining based approach using Self
Organizing Maps (SOM) has been proposed to draw information from the warranty database and to relate it to
the manufacturing data. The clusters obtained using SOM are analyzed to identify the critical regions, i.e.,
sections of the map where maximum defects occur. Finally, to facilitate the correct implementation of design
parameter changes, the frequency and type of defects analyzed from warranty data are used to identify areas
where improvements have resulted in the greatest reliability for the lowest cost. Keywords: Ontology | Self-Organizing Maps | Warranty data | Text mining | Decision support |
مقاله انگلیسی |