با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Fifty years of information management research: A conceptual structure analysis using structural topic modeling
پنجاه سال تحقیقات مدیریت اطلاعات: تجزیه و تحلیل ساختار مفهومی با استفاده از مدل سازی موضوع ساختاری-2021 Information management is the management of organizational processes, technologies, and people which
collectively create, acquire, integrate, organize, process, store, disseminate, access, and dispose of the infor-
mation. Information management is a vast, multi-disciplinary domain that syndicates various subdomains and
perfectly intermingles with other domains. This study aims to provide a comprehensive overview of the infor-
mation management domain from 1970 to 2019. Drawing upon the methodology from statistical text analysis
research, this study summarizes the evolution of knowledge in this domain by examining the publication trends
as per authors, institutions, countries, etc. Further, this study proposes a probabilistic generative model based on
structural topic modeling to understand and extract the latent themes from the research articles related to in-
formation management. Furthermore, this study graphically visualizes the variations in the topic prevalences
over the period of 1970 to 2019. The results highlight that the most common themes are data management,
knowledge management, environmental management, project management, service management, and mobile
and web management. The findings also identify themes such as knowledge management, environmental
management, project management, and social communication as academic hotspots for future research. keywords: مدیریت اطلاعات | مدل های اصلی ساختاری | مدل سازی موضوع | مدل های مولد | تجزیه و تحلیل متن | Information management | Structural topic models | Topic modeling | Generative models | Text analytics |
مقاله انگلیسی |
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Capturing distinctions while mining text data: Toward low-tech formalization for text analysis
گرفتن تمایزات در حین استخراج داده های متنی: به سوی رسمی سازی تکنولوژی کم برای تجزیه و تحلیل متن-2018 In this article we consider some low-tech approaches to text mining. Our goal is to articulate a
RiCH (Reader in Control of Hermeneutics) style of text analysis that takes advantage of the digital
affordances of modern reading practices and easily deployable computational tools while also
preserving the primacy of the interpretive lens of the human reader. In the article we offer three
analytical interventions that are suitable to the low-tech formalizations we propose: the first and
most developed intervention tracks the (normally computationally ignored) “stop” words; the
second identifies the use of strategic anxiety terms in the texts; and the third (less developed in
this article) introduces the grammatical features of modality (including modalization statements
of probability and usuality, and modulation statements regarding degrees of obligation and in
clination). All three analytical interventions provide a productive tracking of various modes and
degrees of strategic decisiveness, contradiction, uncertainty and indeterminacy in a corpus of
recent U.S. National Security Strategy reports.
Keywords: Text mining ، Hermeneutics ، National security ، Computational sociology ، Big data ، Close reading |
مقاله انگلیسی |
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تبدیل محتوای ایجاد شده کاربر مرتبط با سلامت به دانش عملی از طریق خدمات تجزیه و تحلیل متن
Turning user generated health-related content into actionable knowledge through text analytics services-2016 In the last years, the habit of discussing healthcare issues with family and friends, even with unknown people, in the context of social networks has increased and processing user generated content has become a new challenge. This can help in on-line crowd surveillance for different applications (pharmacovigilance and filtering health contents in blogs among others) as well as extracting knowledge from unstructured text sources. In this article, a system that monitors health social media streams is described. It is based on several text analytics processes supported, among others, by MeaningCloud, a commercial platform which provides meaning extraction from texts in a Software as a Service mode. In this architecture, several domain resources are integrated to detect drugs and drug effects such as CIMA (official information about authorized drugs in Spain maintained by the Spanish Agency of Medicines and Health Products), MedDRA (Medical Dictionary for Regulatory Activities) and the SpanishDrugEffectDB database that contains relations between drugs and effects. Different ways of visualizing data considering time lines and aggregated data have been implemented. In order to show performance, an evaluation has been carried out over Named Entities Recognition (NER) and Relation Extraction (RE) tasks.© 2015 Elsevier B.V. All rights reserved. |
مقاله انگلیسی |