Relevant bibliographies by topics / Descriptive Text Mining

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Descriptive Text Mining'

Author: Grafiati
Published: 18 April 2022
Last updated: 30 July 2025

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Journal articles on the topic "Descriptive Text Mining"

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Shin, JongHo, and Jaewon Choi. "Text mining Analysis of College Students’ Descriptive Course Evaluation." Korean Association For Learner-Centered Curriculum And Instruction 19, no. 16 (2019): 77–99. http://dx.doi.org/10.22251/jlcci.2019.19.16.77.

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Teng, Shasha, Kok Wei Khong, Saeed Pahlevan Sharif, and Amr Ahmed. "YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis." JMIR Public Health and Surveillance 6, no. 4 (2020): e19618. http://dx.doi.org/10.2196/19618.

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Background Poor nutrition and food selection lead to health issues such as obesity, cardiovascular disease, diabetes, and cancer. This study of YouTube comments aims to uncover patterns of food choices and the factors driving them, in addition to exploring the sentiments of healthy eating in networked communities. Objective The objectives of the study are to explore the determinants, motives, and barriers to healthy eating behaviors in online communities and provide insight into YouTube video commenters’ perceptions and sentiments of healthy eating through text mining techniques. Methods This
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Lee, In. "Analysis of Insider Threats in the Healthcare Industry: A Text Mining Approach." Information 13, no. 9 (2022): 404. http://dx.doi.org/10.3390/info13090404.

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To address rapidly growing data breach incidents effectively, healthcare providers need to identify various insider and outsider threats, analyze the vulnerabilities of their internal security systems, and develop more appropriate data security measures against the threats. While there have been studies on trends of data breach incidents, there is a lack of research on the analysis of descriptive contents posted on the data breach reporting website of the U.S. Department of Health and Human Services (HHS) Office for Civil Rights (OCR). Hence, this study develops a novel approach to the analysi
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Yunarfi, Geovaldo Reggie, Ricky Simdy, and Jackson. "Implementasi Text Mining untuk Mengetahui Kata Abreviasi dalam Percakapan Media Sosial." Journal of Digital Ecosystem for Natural Sustainability 1, no. 2 (2021): 78–83. https://doi.org/10.63643/jodens.v1i2.43.

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Social media are technology that allow sharing or exchange of information, ideas, interests, etc., via virtual communities and networks. Social media is often use for chatting either private chat or commenting on posts, and most frequently used application in Indonesia such as Facebook, WhatsApp, Instagram, Twitter, and so on. So far, peoples are typing using abbreviations as habit instead using full word and thus cause misunderstanding for others. Descriptive qualitative method was used to collect data. Text mining is a data science technique which mine data in the form of text and look for w
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Kutela, Boniphace, Richard Dzinyela, Henrick Haule, Abbas Sheykhfard, and Kelvin Msechu. "Leveraging autonomous vehicles crash narratives to understand the patterns of parking-related crashes." Traffic Safety Research 4 (July 5, 2023): 000033. http://dx.doi.org/10.55329/fiqq8731.

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Autonomous vehicles (AVs) parking has been a subject of interest from various researchers; however, the focus has been on the parking demand, algorithm, and policies, while the safety aspect has received less attention, perhaps due to the lack of AV crash data. This study evaluated the magnitude and pattern of AV parking-related crashes that occurred between January 2017 and August 2022 in California. The study applied descriptive analysis, unsupervised text mining, and supervised text mining (Support Vector Machine, Naïve Bayes, Logitboost, Random Forest, and Neural network) with resampling t
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Qiu, Longtian, Shan Ning, and Xuming He. "Mining Fine-Grained Image-Text Alignment for Zero-Shot Captioning via Text-Only Training." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 5 (2024): 4605–13. http://dx.doi.org/10.1609/aaai.v38i5.28260.

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Image captioning aims at generating descriptive and meaningful textual descriptions of images, enabling a broad range of vision-language applications. Prior works have demonstrated that harnessing the power of Contrastive Image Language Pre-training (CLIP) offers a promising approach to achieving zero-shot captioning, eliminating the need for expensive caption annotations. However, the widely observed modality gap in the latent space of CLIP harms the performance of zero-shot captioning by breaking the alignment between paired image-text features. To address this issue, we conduct an analysis
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Tsimaras, Dimitrios, Emmanouil Zoulias, and Chryssi Vitsilaki. "Evaluation of Vocational E-Learning Seminars." WSEAS TRANSACTIONS ON ADVANCES in ENGINEERING EDUCATION 20 (February 22, 2023): 32–36. http://dx.doi.org/10.37394/232010.2023.20.5.

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The fourth industrial revolution emerges from a demanding need for reskilling and upskilling every active working person. Furthermore, the European Commission included key policy instruments for resilience, social fairness, and sustainable competitiveness in the European Skills Agenda. Distance training and education programs are key factors to succeed in the targets mentioned above. Due to the o COVID-19 pandemic, already 30% of the total education in European countries has further expanded. As a result, online evaluation approaches are more than necessary. Various methodologies have been app
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ALtom Shihabeldeen, Hassabelrasul Yusuuf. "Using Text Mining to Predicate Exchange Rates with Sentiment Indicators." Journal of Business Theory and Practice 7, no. 2 (2019): p60. http://dx.doi.org/10.22158/jbtp.v7n2p60.

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Recent innovations in text mining facilitate the use of novel data for sentiment analysis related to financial markets, and promise new approaches to the field of behavioral finance. Traditionally, text mining has allowed a near-real time analysis of available news feeds. The recent dissemination of web 2.0 has seen a drastic increase of user participation, providing comments on websites, social networks and blogs, creating a novel source of rich and personal sentiment data potentially of value to behavioral finance. This study explores the efficacy of using novel sentiment indicators from Mar
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Yoon, Sora. "Accounting Studies Using Text Analysis : Now and Future." Korean Accounting Information Association 23, no. 4 (2023): 119–51. http://dx.doi.org/10.29189/kaiajfai.23.4.6.

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[Purpose] The purpose of this study is to examine prior literature using text mining analysis techniques in the accounting area for identifying current research themes, to present other data analysis techniques, and to provide directions for future research and practice.
 [Methodology] For this study, I use the Systematic Literature Review(SLR) methodology adopted in Schmitz and Leoni(2019). The results are obtained by entering keywords of “text analysis’ and “accounting” in Google Scholar, and the prior researches reviewed in this paper are selected among them and their references, mainl
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Longhini, Tatielle Menolli, Marcelo Azevedo Costa, and Bruno de Almeida Vilela. "Satscan regression: a bibliometric study based on text mining procedures." Caderno Pedagógico 22, no. 6 (2025): e15717. https://doi.org/10.54033/cadpedv22n6-214.

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The scan statistic has been a methodology widely applied for spatial and space-time cluster detection. This is because, by identifying areas with a significant concentration of points, we gain evidence for the analysis of underlying phenomena. This article aims to conduct a bibliometric study on spatial scan regression. Spatial scan regression includes correlation between the outcome and potential regressor variables into the cluster detection. The bibliometric study applies a robust literature search methodology, using Web of Science and Scopus as the main databases. Thus, the most relevant a
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Dissertations / Theses on the topic "Descriptive Text Mining"

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Battistini, Riccardo. "Descriptive Text Mining in ambito medico: applicazione della metodologia POIROT." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24781/.

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Poiché la nostra conoscenza collettiva continua ad essere digitalizzata e memorizzata, diventa più difficile trovare e scoprire ciò che stiamo cercando. Abbiamo bisogno di nuovi strumenti computazionali per aiutare a organizzare, rintracciare e comprendere queste vaste quantità di informazioni. I modelli di linguaggio sono potenti strumenti che possono essere impiegati per estrarre conoscenza statisticamente significativa ed interpretabile tramite apprendimento non supervisionato, testuali o nel codice sorgente. L’obiettivo di questa tesi è impiegare una metodologia di descriptive text min
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Frisoni, Giacomo. "A New Unsupervised Methodology of Descriptive Text Mining for Knowledge Graph Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20471/.

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Rare diseases pose particular challenges to patients, families, caregivers, clinicians and researchers. Currently, more than 6000 rare diseases are described (but up to 7000 are estimated) and more than 350 million people live with them (5\% of the world population). Due to the scarce availability of information and their disintegration, in recent years we are witnessing strong growth of patient communities on social platforms such as Facebook. The work presented in this thesis is intended to extract knowledge from the large availability of unstructured text generated by the users over time, i
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Hopton, Sarah Beth. "Evidence of Things Not Seen: A Semi-Automated Descriptive Phrase and Frame Analysis of Texts about the Herbicide Agent Orange." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5705.

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From 1961 to 1971 the United States and the Republic of South Vietnam used chemicals to defoliate the coastal and upload forest areas of Viet Nam. The most notorious of these chemicals was named Agent Orange, a weaponized herbicide made up of two chemicals that, when combined, produced a toxic byproduct called TCDD-dioxin. Studied suggest that TCDD-dioxin causes significant human health problems in exposed American and Vietnamese veterans, and possibly their children (Agency, U.S. Environmental Protection, 2011). In the years since the end of the Vietnam War, volumes of discourse about Agent O
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AbuRa'ed, Ahmed Ghassan Tawfiq. "Automatic generation of descriptive related work reports." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/669975.

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A related work report is a section in a research paper which integrates key information from a list of related scientific papers providing context to the work being presented. Related work reports can either be descriptive or integrative. Integrative related work reports provide a high-level overview and critique of the scientific papers by comparing them with each other, providing fewer details of individual studies. Descriptive related work reports, instead, provide more in-depth information about each mentioned study providing information such as methods and results of the cited works. In
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Deniz, Onur. "Ontology Based Text Mining In Turkish Radiology Reports." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614145/index.pdf.

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Vast amount of radiology reports are produced in hospitals. Being in free text format and having errors due to rapid production, it continuously gets more complicated for radiologists and physicians to reach meaningful information. Though application of ontologies into bio-medical text mining has gained increasing interest in recent years, less work has been offered for ontology based retrieval tasks in Turkish language. In this work, an information extraction and retrieval system based on SNOMED-CT ontology has been proposed for Turkish radiology reports. Main purpose of this work is to uti
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Zois, Christos. "Applying text mining techniques to forecast the stock market fluctuations of large it companies with twitter data: descriptive and predictive approaches to enhance the research of stock market predictions with textual and semantic data." Master's thesis, 2019. http://hdl.handle.net/10362/92164.

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Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management<br>This research project applies advanced text mining techniques as a method to predict stock market fluctuations by merging published tweets and daily stock market prices for a set of American Information Technology companies. This project executes a systematical approach to investigate and further analyze, by using mainly R code, two main objectives: i) which are the descriptive criteria, patterns, and variables, which
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"Understanding the Determinants of Success in Mobile Apps Markets." Doctoral diss., 2015. http://hdl.handle.net/2286/R.I.29811.

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abstract: Mobile applications (Apps) markets with App stores have introduced a new approach to define and sell software applications with access to a large body of heterogeneous consumer population. Several distinctive features of mobile App store markets including – (a) highly heterogeneous consumer preferences and values, (b) high consumer cognitive burden of searching a large selection of similar Apps, and (c) continuously updateable product features and price – present a unique opportunity for IS researchers to investigate theoretically motivated research questions in this area. The aim
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Paquette-Bigras, Ève. "Étude comparative du vocabulaire de description de la danse dans les archives et du vocabulaire de représentation de la danse dans la littérature." Thèse, 2013. http://hdl.handle.net/1866/9938.

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Notre recherche s’insère dans la mouvance des humanités numériques; nous y faisons dialoguer les arts et les sciences de l’information. Depuis quelques décennies, la danse est un sujet d’études et de recherche à part entière. Il devient donc nécessaire de mieux décrire la danse dans les archives, sachant que la description en amont influe grandement sur l’accès en aval. Les méthodes d’extraction automatique de connaissances nous semblent offrir de nouvelles possibilités. L’objectif de notre recherche est de contribuer au développement d’outils de gestion de l’information dans les archives de l
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Newsom, Eric Tyner. "An exploratory study using the predicate-argument structure to develop methodology for measuring semantic similarity of radiology sentences." Thesis, 2013. http://hdl.handle.net/1805/3666.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>The amount of information produced in the form of electronic free text in healthcare is increasing to levels incapable of being processed by humans for advancement of his/her professional practice. Information extraction (IE) is a sub-field of natural language processing with the goal of data reduction of unstructured free text. Pertinent to IE is an annotated corpus that frames how IE methods should create a logical expression necessary for processing meaning of text. Most annotation approaches seek to maximize meaning and knowledge
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Books on the topic "Descriptive Text Mining"

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Williams, Robert S. Description of techniques to drill, complete, and develop wells and to test and sample aquifers at a surface coal mine in northwestern Colorado. U.S. Dept. of the Interior, U.S. Geological Survey, 1993.

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Hodges, John R. Standardized Mental Test Schedules. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198749189.003.0006.

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This chapter discusses standardized mental test schedules. Many brief standardized assessment tools have been advocated over the past two or three decades but many have largely fallen into disuse. This chapter focuses, therefore, on two of the most commonly used brief assessment schedules: the Mini-Mental State Examination (MMSE), and the Montreal Cognitive Assessment (MoCA) with an analysis of their strengths, weaknesses, and applications. The MoCA is freely available and a link to the website is included. They are contrasted with the ACE-III which is described in Chapter 7. The chapter also
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Hodges, John R. Standardized Mental Test Schedules: Their Uses and Abuses. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780192629760.003.0006.

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Chapter 6 discusses various mental test schedules used over the years, from the 10-item Hodgkinson Mental Test, to the much more complex Dementia Rating Scale (DRS. For practical purposes, however, such tests can be divided into two broad groups: (i) the brief schedules that can easily be used in the clinic, or at the bedside, and do not require specialized equipment or training, and (ii) the more elaborate scales, which are used largely, at least at present, in research studies, and require the purchase of test materials and some training in their administration. The Addenbrooke’s Cognitive E
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Owen, Gareth, Sir Simon Wessely, and Sir Simon Wessely, eds. The mental state examination. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199661701.003.0004.

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The chapter outlines traditional categories used to capture the current condition of a patient’s state of mind such as appearance and behaviour, mood, speech, thought content, abnormal beliefs and experiences. It suggests questions to ask and how to organize the material. The chapter encourages a descriptive approach in which examples from the interview are recorded to help future reference and help other clinicians make judgements as to significance. Guidance is also given on examining the cognitive state and interpreting intelligence. Further advice is offered on examining the mental state i
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Book chapters on the topic "Descriptive Text Mining"

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Ariza-Colpas, Paola, Ana Isabel Oviedo-Carrascal, and Emiro De-la-hoz-Franco. "Using K-Means Algorithm for Description Analysis of Text in RSS News Format." In Data Mining and Big Data. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9563-6_17.

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Stalidis, George, and Selini Kyriazidou. "Job Role Description and Skill Matching in a Rapidly Changing Labor Market Using Knowledge Engineering." In Strategic Innovative Marketing and Tourism. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51038-0_21.

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AbstractThe job market is continuously evolving and suffers from skill mismatch, while digital platforms for job seeking, human resource management and training planning are widely adopting intelligent matching engines. Efficient job description and successful skill matching require standard terminologies for job positions and their requirements/qualifications, such as the ‘European Skills, Competences, Qualifications and Occupations’ (ESCO). The aim of this paper is to search whether ESCO—as the most representative job-related information model—has been adopted in the current rapidly evolving
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Sheik Abdullah A, Selvakumar S, and Ramya C. "Descriptive Analytics." In Advances in Business Information Systems and Analytics. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2148-8.ch006.

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Data analytics has becoming one of the challenging platforms across various domains such as telecom, health care, social media and so on. The challenging and most promising task in analytics is the understanding of various patterns in the data. The mechanism of data retrieval and analysis seems to be the promising one in which the algorithms, techniques, way of processing data are in need with the ability to target upon large volumes of data. There are various types of analytical methods such as predictive analytics, descriptive analytics, text analytics, social media analytics and survival an
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Mantovani, Daielly M. N., Kleber Rodrigues Santos, Thaisa Barcellos Pinheiro Nascimento, and Celso Machado Jr. "Engaging People on E-Participation Through Social Media Interactions." In Handbook of Research on Solving Societal Challenges Through Sustainability-Oriented Innovation. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6123-5.ch015.

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The chapter aims to analyze how social media engages citizens in issues related to municipal management in Brazilian capital cities (27 cities). For that, Twitter data was collected, and descriptive analysis, text mining, and social network analysis were carried out. Results show the most frequent interactions regarded sharing posts, replies, and reactions were less frequent. Text mining suggested behavior on Twitter is related on the hot news, so discussions tend to be superficial; network analysis showed mayor accounts have more connections with users than the cities' official accounts, whic
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Kartal, Burcu, and Mehmet Fatih Sert. "Review of the Studies Related to COVID-19 and Tourism Using Text Mining Techniques." In Handbook of Research on the Impacts and Implications of COVID-19 on the Tourism Industry. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8231-2.ch043.

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This study aims to provide a roadmap for research dealing with the tourism sector. In this context, by conducting a study in the form of a literature review, researchers are informed about what has been done and what is missing. In the study, articles that have been accepted from scientific journals indexed in the SCOPUS database before January 18, 2021 and dealing with COVID-19 and tourism issues are examined. The study was carried out in two stages. In the first stage, descriptive statistics were given in terms of the region studied in the articles, the journal in which the articles were pub
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Gupta, Rajan, Saibal Kumar Pal, and Sunil Kumar Muttoo. "Data Analytics for Better Branding of E-Governance and E-Business Systems." In Advances in Electronic Government, Digital Divide, and Regional Development. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-5412-7.ch003.

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Efficient e-governance leads to stronger democracy which can be achieved through higher trust, visibility, and transparency in the system, which can be acquired through effective branding. Various techniques of data analytics can help in achieving trust and transparency in the system. The objective of the study is to resolve various issues in the public sector through analytics-based improvement of different parameters of branding, namely, communication, consistency, clarity, and competition. The research design of the study is a combination of both qualitative and quantitative techniques like
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"Answer Evaluation of Short Descriptive Questions." In Advances in Data Mining and Database Management. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3772-5.ch005.

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Reforms in the educational system emphasize more on continuous assessment. The descriptive examination test paper when compared to objective test paper acts as a better aid in continuous assessment for testing the progress of a student under various cognitive levels at different stages of learning. Unfortunately, assessment of descriptive answers is found to be tedious and time-consuming by instructors due to the increase in number of examinations in continuous assessment system. In this chapter, an attempt has been made to address the problem of automatic evaluation of descriptive answer usin
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Öztaysi, Basar, and Sezi Çevik Onar. "User Segmentation Based on Twitter Data Using Fuzzy Clustering." In Data Mining in Dynamic Social Networks and Fuzzy Systems. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-4213-3.ch014.

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Social Networking Sites, which create platform for social interactions and sharing are the mostly used internet websites, thus are very important in today’s world. The vast usage of social networking sites (SNSs) has effected the business world, new business models are proposed, business process are renewed and companies try to create benefit form these sites. Besides the functional usage of SNSs such as marketing and customer relations, companies can create value by analyzing and mining the data on SNSs. In this paper, a new segmentation approach, using Text Mining and Fuzzy Clustering techni
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Dileep, A. D., T. Veena, and C. Chandra Sekhar. "A Review of Kernel Methods Based Approaches to Classification and Clustering of Sequential Patterns, Part I." In Data Mining. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2455-9.ch013.

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Sequential data mining involves analysis of sequential patterns of varying length. Sequential pattern analysis is important for pattern discovery from sequences of discrete symbols as in bioinformatics and text analysis, and from sequences or sets of continuous valued feature vectors as in processing of audio, speech, music, image, and video data. Pattern analysis techniques using kernel methods have been explored for static patterns as well as sequential patterns. The main issue in sequential pattern analysis using kernel methods is the design of a suitable kernel for sequential patterns of v
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Behdenna, Salima, Fatiha Barigou, and Ghalem Belalem. "Towards Semantic Aspect-Based Sentiment Analysis for Arabic Reviews." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6303-1.ch077.

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Sentiment analysis is a text mining discipline that aims to identify and extract subjective information. This growing field results in the emergence of three levels of granularity (document, sentence, and aspect). However, both the document and sentence levels do not find what exactly the opinion holder likes and dislikes. Furthermore, most research in this field deals with English texts, and very limited researches are undertaken on Arabic language. In this paper, the authors propose a semantic aspect-based sentiment analysis approach for Arabic reviews. This approach utilizes the semantic of
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Conference papers on the topic "Descriptive Text Mining"

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Rehor, Michal, Pavel Schmidt, Radka Duzekov, and Petr Vrablik. "DEGRADED SOILS OF THE MOST BASIN AND THE POSSIBILITIES OF THEIR LOCATION, CLASSIFICATION AND RECLAMATION." In 24th SGEM International Multidisciplinary Scientific GeoConference 2024. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/3.1/s13.25.

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Brown Coal Research Institute j. v. c. (VUHU) from July 2023, together with scientific teams from several European countries, participates as part of the EU research program in the solution of the REECOL project, which deals with the reclamation of areas affected by mining in European coal basins. This paper includes the results achieved in the preparation of the first sub-research report of the project, which deals with degraded soils. On the basis of archival data and own research, the concept of "soil degradation" was defined for the purposes of the REECOL project solution in the conditions
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Frisoni, Giacomo, Gianluca Moro, and Antonella Carbonaro. "Unsupervised Descriptive Text Mining for Knowledge Graph Learning." In 12th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0010153603100318.

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Frisoni, Giacomo, Gianluca Moro, and Antonella Carbonaro. "Unsupervised Descriptive Text Mining for Knowledge Graph Learning." In 12th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0010153603160324.

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Mangmang, Geraldine B., Larmie Feliscuzo, and Elmer A. Maravillas. "Descriptive Feedback on Interns’ Performance using a text mining approach." In 2019 14th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP). IEEE, 2019. http://dx.doi.org/10.1109/isai-nlp48611.2019.9045604.

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Frisoni, Giacomo, Gianluca Moro, and Antonella Carbonaro. "Learning Interpretable and Statistically Significant Knowledge from Unlabeled Corpora of Social Text Messages: A Novel Methodology of Descriptive Text Mining." In 9th International Conference on Data Science, Technology and Applications. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009892001210132.

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Romana García, María Luisa, and Blanca Hernández Pardo. "Characterization of Institutional Texts for an Automated Golden Standard: Enhancing Machine Translation Quality Assessment between English and Spanish." In New Trends in Translation and Technology Conference 2024. INCOMA Ltd. Shoumen, BULGARIA, 2024. http://dx.doi.org/10.26615/issn.2815-4711.2024_012.

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The purpose of this paper is to collect a set of features that can contribute to the linguistic characterization of the institutional textual genre. The aim is to describe as exhaustively as possible the archetypal text to be obtained as a target text in this type of specialized translation. The tools used were Orange Data Mining© and Google Colab (Python code), and the data was obtained using the following processing mechanisms: word cloud, text preprocessing (cleaning, tokenization, normalization, lemmatization and PoS annotation). With these tools, lexical and grammatical frequencies, lexic
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Wang, Han, and Zefeng Li*. "The application of machine learning and deep learning to Ophthalmology: A bibliometric study (2000-2021)." In Human Interaction and Emerging Technologies (IHIET-AI 2022) Artificial Intelligence and Future Applications. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe100885.

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Machine learning (ML) and deep learning (DL) are an advanced technology for the latest 20 years, which has been applied for multiple fields. This study utilizes methods of text mining and bibliometric analysis to explore applications of ML and DL to Ophthalmology. 50-ML-related and 60-DL-related papers from Web of Science (WOS), 15-ML-related and 38-DL-related articles from China Nation-al Knowledge Infrastructure (CNKI) are explored from 2000 to 2021. A descriptive analysis of major article, developing trends, journal releasing, topic mapping and quotation relationships is implemented in this
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Chang, Shun-Chuan. "ANALYZING PUBLIC HEALTH CONCERNS THROUGH TEXT MINING AND SOCIAL NETWORK ANALYSIS: A CASE STUDY OF COVID-19 PUBLIC OPINION ANALYSIS FROM ONLINE COMMUNITY FORUMS IN TAIWAN." In MBP 2024 Tokyo International Conference on Management & Business Practices, 18-19 January. Global Research & Development Services, 2024. http://dx.doi.org/10.20319/icssh.2024.7677.

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In the past, many quantitative studies in public health relied on traditional descriptive statistical data and less on analyzing unstructured text data. However, in the era of close online communication, a huge amount of text information related to public health issues is generated in online communities every day. COVID-19 pandemic should be one of the most important public health events in Taiwan since 2020. Many people express their views and feelings on the epidemic issue in online forums. In this research, the aim is to apply text mining and social network analysis to analyze the sentiment
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Mušanović, Jelena, Raffaella Folgieri, and Maja Gregorić. "SENTIMENT ANALYSIS AND MULTIMODAL APPROACH APPLIED TO SOCIAL MEDIA CONTENT IN HOSPITALITY INDUSTRY." In Tourism in Southern and Eastern Europe 2021: ToSEE – Smart, Experience, Excellence & ToFEEL – Feelings, Excitement, Education, Leisure. University of Rijeka, Faculty of Tourism and Hospitality Management, 2021. http://dx.doi.org/10.20867/tosee.06.36.

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Purpose – The importance of the "data gold rush" that occurs in real time on various social media platforms is recognized by various tourism stakeholders and researcher. To extract knowledge from textual data, the purpose of this study is to apply text mining techniques to social media data. Methodology – Descriptive statistical analysis is conducted to quantify the activity of hotel brands on Facebook. The topic modelling technique Latent Dirichlet Allocation (LDA) is used to extract and validate knowledge from text data of 25 Croatian four- and five- star hotel brands that were active on soc
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Fourati, Manel, Anis Jedidi, and Faiez Gargouri. "Automatic Audiovisual Documents Genre Description." In Special Session on Text Mining. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0005170905380543.

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