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Journal articles on the topic 'Unstructured text data'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Nasibah, Husna Mohd Kadir, and Aliman Sharifah. "Text analysis on health product reviews using r approach." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 18, no. 3 (2020): 1303–10. https://doi.org/10.11591/ijeecs.v18.i3.pp1303-1310.

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In the social media, product reviews contain of text, emoticon, numbers and symbols that hard to identify the text summarization. Text analytics is one of the key techniques in exploring the unstructured data. The purpose of this study is solving the unstructured data by sort and summarizes the review data through a Web-Based Text Analytics using R approach. According to the comparative table between studies in Natural Language Processing (NLP) features, it was observed that Web-Based Text Analytics using R approach can analyze the unstructured data by using the data processing package in R. I

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AL-Mashhadany, Abeer K., Dalal N. Hamood, Ahmed T. Sadiq Al-Obaidi, and Waleed K. Al-Mashhadany. "Extracting numerical data from unstructured Arabic texts(ENAT)." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 3 (2021): 1759–70. https://doi.org/10.11591/ijeecs.v21.i3.pp1759-1770.

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Unstructured data becomes challenges because in recent years have observed the ability to gather a massive amount of data from annotated documents. This paper interested with Arabic unstructured text analysis. Manipulating unstructured text and converting it into a form understandable by computer is a high-level aim. An important step to achieve this aim is to understand numerical phrases. This paper aims to extract numerical data from Arabic unstructured text in general. This work attempts to recognize numerical characters phrases, analyze them and then convert them into integer values. The i

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Shastri, Shankarayya, Teligi Math Veeragangadhara Swamy, and Siddalingappa Patil Nagaraja. "Sensing complicated meanings from unstructured data: a novel hybrid approach." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 711–20. https://doi.org/10.11591/ijece.v14i1.pp711-720.

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The majority of data on computers nowadays is in the form of unstructured data and unstructured text. The inherent ambiguity of natural language makes it incredibly difficult but also highly profitable to find hidden information or comprehend complex semantics in unstructured text. In this paper, we present the combination of natural language processing (NLP) and convolution neural network (CNN) hybrid architecture called automated analysis of unstructured text using machine learning (AAUT-ML) for the detection of complex semantics from unstructured data that enables different users to make un

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Oh, Tae-Jin, and Anthony. "New and Fast Emerging Advance Structure of Text Mining from Unstructured Data." Bonfring International Journal of Industrial Engineering and Management Science 7, no. 2 (2017): 13–16. http://dx.doi.org/10.9756/bijiems.8325.

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Muhammad, Aoun. "Comparative Analysis of Text Mining Techniques for News Article Summarization." LC International Journal of STEM (ISSN: 2708-7123) 4, no. 1 (2023): 52–63. https://doi.org/10.5281/zenodo.7893329.

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Text mining research paper is a scientific study that focuses on the development and application of text mining techniques for extracting valuable information from unstructured textual data. The paper discusses the challenges of working with unstructured data and the need for advanced text mining techniques to address these challenges. The paper outlines the various steps involved in the text mining process, such as data preprocessing, text representation, and feature selection. It discusses the importance of selecting appropriate algorithms for different types of text mining tasks, including

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B, L. Shilpa, and R. Shambhavi B. "Structuring of Unstructured Data from Heterogeneous Sources." Indian Journal of Science and Technology 15, no. 41 (2022): 2188–93. https://doi.org/10.17485/IJST/v15i41.1566.

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Abstract <strong>Objectives:</strong> To develop a new data gathering processing under Big Data Perspectives. To convert unstructured text data into structured format by not missing out any text data available.<strong> Methods:</strong> The unstructured data is preprocessed using modified stemming and tokenization. From the stemming output, the proposed Term Frequency-Inverse Document Frequency (TF-IDF) and N-gram features are derived. Unstructured data is considered from multiple sources like twitter, consumer complaints and news blog. <strong>Findings:</strong> The p

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Zhai, Yanrui, Xiran Zhou, and Honghao Li. "Model and Data Integrated Transfer Learning for Unstructured Map Text Detection." ISPRS International Journal of Geo-Information 12, no. 3 (2023): 106. http://dx.doi.org/10.3390/ijgi12030106.

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The emergence of the third information wave makes extensive maps available to be generated by volunteered ways, never specially designed and generated by professional institutes alone. These large-scale images-based volunteered maps created by the public provide plentiful geographical information regarding a place while posing a challenge for recognizing the unstructured text in these maps for previous approaches to standard map text detection. Map text or map annotations denote the critical element of map content. To achieve the detection of unstructured map text, this paper proposed an integ

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Shastri, Shankarayya, Veeragangadhara Swamy Teligi Math, and Patil Nagaraja Siddalingappa. "Sensing complicated meanings from unstructured data: a novel hybrid approach." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 1 (2024): 711. http://dx.doi.org/10.11591/ijece.v14i1.pp711-720.

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The majority of data on computers nowadays is in the form of unstructured data and unstructured text. The inherent ambiguity of natural language makes it incredibly difficult but also highly profitable to find hidden information or comprehend complex semantics in unstructured text. In this paper, we present the combination of natural language processing (NLP) and convolution neural network (CNN) hybrid architecture called automated analysis of unstructured text using machine learning (AAUT-ML) for the detection of complex semantics from unstructured data that enables different users to make un

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Ali, Hameed Yassir, A. Mohammed Ali, Abdul-Jabbar Alkhazraji Adel, Emad Hameed Mustafa, Saad Talib Mohammed, and Faeq Ali Mohanad. "Sentimental classification analysis of polarity multi-view textual data using data mining techniques." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5526–34. https://doi.org/10.11591/ijece.v10i5.pp5526-5534.

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The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats.

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Côté, Jean, John H. Salmela, Abderrahim Baria, and Storm J. Russell. "Organizing and Interpreting Unstructured Qualitative Data." Sport Psychologist 7, no. 2 (1993): 127–37. http://dx.doi.org/10.1123/tsp.7.2.127.

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In the last several years there has been an increase in the amount of qualitative research using in-depth interviews and comprehensive content analyses in sport psychology. However, no explicit method has been provided to deal with the large amount of unstructured data. This article provides common guidelines for organizing and interpreting unstructured data. Two main operations are suggested and discussed: first, coding meaningful text segments, or creating tags, and second, regrouping similar text segments, or creating categories. Furthermore, software programs for the microcomputer are pres

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Singh, Shashi Pal, Ajai Kumar, Rachna Awasthi, Neetu Yadav, and Shikha Jain. "Intelligent Bilingual Data Extraction and Rebuilding Using Data Mining for Big Data." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 513–18. http://dx.doi.org/10.1166/jctn.2020.8699.

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In today’s World there exists various source of data in various formats (file formats), different structure, different types and etc. which is a hug collection of unstructured over the internet or social media. This gives rise to categorization of data as unstructured, semi structured and structured data. Data that exist in irregular manner without any particular schema are referred as unstructured data which is very difficult to process as it consists of irregularities and ambiguities. So, we are focused on Intelligent Processing Unit which converts unstructured big data into intelligent mean

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K. AL-Mashhadany, Abeer, Dalal N. Hamood, Ahmed T. Sadiq Al-Obaidi, and Waleed K. Al-Mashhsdany. "Extracting numerical data from unstructured Arabic texts (ENAT)." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 3 (2021): 1759. http://dx.doi.org/10.11591/ijeecs.v21.i3.pp1759-1770.

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<span id="docs-internal-guid-5dcc170c-7fff-e8e4-10d4-4a07701ca923"><span>Unstructured data becomes challenges because in recent years have observed the ability to gather a massive amount of data from annotated documents. This paper interested with Arabic unstructured text analysis. Manipulating unstructured text and converting it into a form understandable by computer is a high-level aim. An important step to achieve this aim is to understand numerical phrases. This paper aims to extract numerical data from Arabic unstructured text in general. This work attempts to recognize numeri

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Ranjan, Nihar M., and Rajesh S. Prasad. "Text Analytics: An Application of Text Mining." Journal of Data Mining and Management 6, no. 3 (2021): 1–6. http://dx.doi.org/10.46610/jodmm.2021.v06i03.001.

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About 80% organizational data are present in the unstructured (Text) format. E-mails, social media, notes, and wide variety of different types of documents in text formats are present, but all these data are not get importance and analyzed in meaningful ways. It has been observed that information workers spend their significant time (up to one third) to locating this information and trying to make sense of it. Text analytics is the process which analyzed all these available unstructured text information and converts it into useful information which helps the organization significantly in their

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Gogo, Ngor, Matthias Daniel, and Alabo Gift. "Giving Structure to Unstructured Text Data by Employing Classification." International Journal of Computer Trends and Technology 69, no. 2 (2021): 22–28. http://dx.doi.org/10.14445/22312803/ijctt-v69i2p104.

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Kim, Geun-hyung, Seongmo Yang, Jihoon Kang, Jin-eun Jeong, and Seung Hwan Park. "Analysis of Weapon System Unstructured Data Using Text Mining." Journal of Applied Reliability 20, no. 4 (2020): 349–56. http://dx.doi.org/10.33162/jar.2020.12.20.4.349.

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Zainol, Zuraini, Mohd T. H. Jaymes, and Puteri N. E. Nohuddin. "VisualUrText: A Text Analytics Tool for Unstructured Textual Data." Journal of Physics: Conference Series 1018 (May 2018): 012011. http://dx.doi.org/10.1088/1742-6596/1018/1/012011.

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Wang, Jiangping. "Extracting Value from Unstructured Data – Implementing Text Analytics on the Voice of Student." Transactions on Machine Learning and Artificial Intelligence 8, no. 4 (2020): 14–22. http://dx.doi.org/10.14738/tmlai.84.8456.

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Unstructured data is chaotic and messy with little or no metadata and lacks of traditional organization structure. However, same as any structured data, unstructured data is also part of valuable business asset. Many times, it is text heavy and needs extensive preprocessing before data mining algorithm can apply for building models in order to reveal value hidden in the data. Text as a form of data is widely used in business operations as a major way of communication, generating increasing volumes of data. Text data in its raw form is relatively dirty. The embedded business value can be extrac

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Adnan, Kiran, and Rehan Akbar. "Limitations of information extraction methods and techniques for heterogeneous unstructured big data." International Journal of Engineering Business Management 11 (January 1, 2019): 184797901989077. http://dx.doi.org/10.1177/1847979019890771.

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During the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation. Effective use of these unstructured big data is a laborious and tedious task. Information extraction (IE) systems help to extract useful information from this large variety of unstructured data. Several techniques and methods have been presented for IE from unstructured data. However, numerous studies conducted on IE from a variety of unstructured data are limited to single data types such as text, image, audio, or video. This article reviews t

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Axel, Rodríguez-García, and Jipsion Armando. "Graph Model for Detection of text unstructured data such as Sarcasm." Latin-American Journal of Computing 8, no. 1 (2021): 70–91. https://doi.org/10.5281/zenodo.5747781.

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Sarcasm is frequently characterized as verbal incongruity to communicate scorn. It is a nuanced type of language with which people express something contrary to what is suggested. Perhaps the greatest test in building frameworks to consequently recognize unstructured information, for example, mockery, is the absence of huge, commented on informational indexes. We propose a diagram-based procedure in building conservative language models for sarcasm recognition. This strategy is likewise intended to utilize little information, it could help in different regions like disdain discourse, counterfe

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Bounabi, Mariem, Karim EL Moutaouakil, and Khalid Satori. "The Optimal Inference Rules Selection for Unstructured Data Multi-Classification." Statistics, Optimization & Information Computing 10, no. 1 (2022): 225–35. http://dx.doi.org/10.19139/soic-2310-5070-1131.

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The Fuzzy Inference System (FIS) is frequently utilized in a variety of Text Mining applications. In the text processing domains, where the amount of the processed data is vast, inserting manual rules for FIS remains a real issue, especially in the text processing domains, where the size of the processed databases is enormous. Therefore, an automated and optimal inference rules (IR) selection strengthens the FIS process. In this work, we propose to apply the FP-Growth as an association model algorithm and an automatic way to identify IR for fuzzy text vectorization. Once the fuzzy vectors are

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A R, Anusha. "Novel Approach to Transform Unstructured Healthcare Data to Structured Data." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 2798–802. http://dx.doi.org/10.22214/ijraset.2021.36972.

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With the rapid growth in number and dimension of databases and database applications in Healthcare records, it is necessary to design a system to achieve automatic extraction of facts from huge table. At the same point, there is a provocation in controlling unstructured data as it highly difficult to analyze and extract actionable intelligence. Preprocessing is an important task and critical step in Text Mining, Regular Expression and Information retrieval. The accession of key data from unstructured data is often difficult. The objective of this project is to transform the unstructured health

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Ambhore, Rajashree. "Governance of Unstructured Data: Managing Data Quality in Non-Traditional Data Sources." International Journal of Research 11, no. 12 (2024): 19–37. https://doi.org/10.5281/zenodo.14283398.

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<em>In an era defined by exponential data growth, unstructured data now constitutes over 80% of all data generated globally, including diverse formats like text, video, audio, and social media posts. Despite its potential value, unstructured data presents unique governance challenges due to its complexity and lack of standardization. This study explores the importance of governing unstructured data, particularly from non-traditional sources like IoT devices and social media, emphasizing strategies for maintaining data quality, integrity, and security. Through an analysis of current frameworks

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M.Karthica and Dr.K. Meenakshi Sundaram. "A Comparative Analysis of Text Mining Techniques and Algorithms." International Journal for Modern Trends in Science and Technology 9, no. 01 (2023): 54–61. http://dx.doi.org/10.46501/ijmtst0901010.

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With the abundant technological progression and its colossal consumption develops the gigantic quantity of unstructured text data digitally. This type of data controlluxurious information as well as knowledge. Therefore, in order to extract such an amount of knowledge from unstructured text data, a data expert involve to perform mining techniques over textual data. Text mining is the procedure of extracting hidden, priory unidentified, as well asconsiderablyutilizeful information from unstructured textual data.Web browsers became an significantas well as implement to create the information ava

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Lee, Jong Hwa, and Hyun-Kyu Lee. "A study on unstructured text mining algorithm through R programming based on data dictionary." Journal of the Korea Industrial Information Systems Research 20, no. 2 (2015): 113–24. http://dx.doi.org/10.9723/jksiis.2015.20.2.113.

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Zheng, Minyi. "Advanced Artificial Intelligence Model for Financial Accounting Transformation Based on Machine Learning and Enterprise Unstructured Text Data." Mobile Information Systems 2022 (July 30, 2022): 1–11. http://dx.doi.org/10.1155/2022/5708652.

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Machine learning belongs to the science of artificial intelligence, so its main exploration goal is artificial intelligence, mainly to accumulate experience and improve the relevant performance of the algorithm. ML is a complex discipline that learns and improves skills primarily through imitation. Unstructured text data refer to unstructured data in the form of text. Financial accounting is a basic work of an enterprise, mainly to provide decision-making reference information for enterprise managers to ensure the normal operation of the enterprise. Financial accounting is a management activit

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Goswami, Mausumi, and B.S Purkayastha. "AN EMPIRICAL ANALYSIS OF SIMILARITY MEASURES FOR UNSTRUCTURED DATA." COMPUSOFT: An International Journal of Advanced Computer Technology 08, no. 08 (2019): 3302–6. https://doi.org/10.5281/zenodo.14832795.

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With fast growth in size of digital text documents over internet and digital repositories, the pools of digital document is piling up day by day. Due to this digital revolution and growth, an efficient and effective technique is required to handle such an enormous amount of data. It is extremely important to understand the documents properly to mine them. To find coherence among documents text similarity measurement pays a humongous role. The goal of similarity computation is to identify cohesion among text documents and to make the text ready for the required applications such as document org

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Goswami, Mausumi, and B.S Purkayastha. "AN EMPIRICAL ANALYSIS OF SIMILARITY MEASURES FOR UNSTRUCTURED DATA." COMPUSOFT: An International Journal of Advanced Computer Technology 08, no. 08 (2019): 3302–6. https://doi.org/10.5281/zenodo.14832840.

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With fast growth in size of digital text documents over internet and digital repositories, the pools of digital document is piling up day by day. Due to this digital revolution and growth, an efficient and effective technique is required to handle such an enormous amount of data. It is extremely important to understand the documents properly to mine them. To find coherence among documents text similarity measurement pays a humongous role. The goal of similarity computation is to identify cohesion among text documents and to make the text ready for the required applications such as document org

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Ahmed, Adeeb Jalal, Ahmed Jasim Abdulrahman, and A. Mahawish Amar. "A web content mining application for detecting relevant pages using Jaccard similarity." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (2022): 6461–71. https://doi.org/10.11591/ijece.v12i6.pp6461-6471.

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The tremendous growth in the availability of enormous text data from a variety of sources creates a slew of concerns and obstacles to discovering meaningful information. This advancement of technology in the digital realm has resulted in the dispersion of texts over millions of web sites. Unstructured texts are densely packed with textual information. The discovery of valuable and intriguing relationships in unstructured texts demands more computer processing. So, text mining has developed into an attractive area of study for obtaining organized and useful data. One of the purposes of this res

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Sheshasaayee, Ananthi, and R. Jayanthi. "Exploring the potential of Social Media Data using Text Mining to augment Business Intelligence." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 04 (2014): 738–42. https://doi.org/10.5281/zenodo.14741740.

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In recent years, social media has become world-wide famous and important for content sharing, social networking, etc., The contents generated from these websites remains largely unused. Social media contains text, images, audio, video, and so on. Social media data largely contains unstructured text. Foremost thing is to extract the information in the unstructured text. This paper presents the influence of social media data for research and how the content can be used to predict real-world decisions that enhance business intelligence, by applying the text mining methods. 

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Thomas, David A. "Searching for Significance in Unstructured Data: Text Mining with Leximancer." European Educational Research Journal 13, no. 2 (2014): 235–56. http://dx.doi.org/10.2304/eerj.2014.13.2.235.

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PU, Hai-xia, Jia-tian LI, Rui LI, Yu-feng HE, and Hua WANG. "Descriptive query method based on unstructured text data in GIS." Journal of Computer Applications 32, no. 9 (2013): 2483–87. http://dx.doi.org/10.3724/sp.j.1087.2012.02483.

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Sarannya, S., M. Venkatesan, and Prabhavathy Panner. "Double Clustering Based Neural Feedback Method for Unstructured Text Data." Journal of Computational and Theoretical Nanoscience 18, no. 4 (2021): 1306–11. http://dx.doi.org/10.1166/jctn.2021.9385.

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Text clustering has now a days become a very major technique in many fields including data mining, Natural Language Processing etc. It’s also broadly used for information retrieval and assimilation of textual data. Majority of the works which were carried out previously focuses on the clustering algorithms where feature extraction is done without considering the semantic meaning of word based on its context. In the given work, we introduce a double clustering algorithm using K -Means, by using in conjuction, a Bi-directional Long Short-Term Memory and a Convolutional Neural Network for the pur

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Borodkin, Artem, Evgeny Lisin, and Wadim Strielkowski. "Data algorithms for processing and analysis of unstructured text documents." Applied Mathematical Sciences 8 (2014): 1213–22. http://dx.doi.org/10.12988/ams.2014.4125.

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Лавлинский, В., V. Lavlinskiy, Юлия Зольникова, and Yuliya Zol'nikova. "INFORMATION SYSTEMS FOR EXTRACTING DATA FROM UNSTRUCTURED TEXT USING ONTOLOGIES." Modeling of systems and processes 11, no. 3 (2019): 30–34. http://dx.doi.org/10.12737/article_5c4f196e58e605.96494978.

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Kahya-Özyirmidokuz, Esra. "Analyzing unstructured Facebook social network data through web text mining." Information Development 32, no. 1 (2014): 70–80. http://dx.doi.org/10.1177/0266666914528523.

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Jeong, Wuseong, JungJin Kim, and Hanseok Jeong. "Information Extraction from Unstructured Data on Microplastics through Text Mining." Journal of Korean Society of Environmental Engineers 45, no. 1 (2023): 34–42. http://dx.doi.org/10.4491/ksee.2023.45.1.34.

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Objectives:In this study, we seek to provide a thorough insight into how people perceive microplastics and uncover issues and hidden trends about the significant microplastic pollution problems by analyzing unstructured data on microplastics.Methods:Environmental news articles related to microplastics were collected. Text mining techniques including data pre-processing, word cloud, TF-IDF weight-based trend analysis, and LDA topic modeling were used to analyze the amount of textual data.Results and Discussion:The public's interest in microplastics is consistently growing, according to an analy

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Kumar, Akshi, Vikrant Dabas, and Parul Hooda. "Text classification algorithms for mining unstructured data: a SWOT analysis." International Journal of Information Technology 12, no. 4 (2018): 1159–69. http://dx.doi.org/10.1007/s41870-017-0072-1.

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Devendra, Kumar Mishra*1. "CHALLENGES IN TEXT MINING FOR BUSINESS INTELLIGENCE." International Journal of Engineering Technologies and Management Research 5, no. 2 (SE) (2018): 301–4. https://doi.org/10.5281/zenodo.1247479.

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Today is the era of internet; the internet represents a big space where large amounts of data are added every day. This huge amount of digital data and interconnection exploding data. Big Data mining have the capability to retrieving useful information in large datasets or streams of data. Analysis can also be done in a distributed environment. The framework needed for analysis to this large amount of data must support statistical analysis and data mining. The framework should be design in such a way so that big data and traditional data can be combined, so results that come analyzing new data

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Mohd Kadir, Nasibah Husna, and Sharifah Aliman. "Text analysis on health product reviews using r approach." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 3 (2020): 1303. http://dx.doi.org/10.11591/ijeecs.v18.i3.pp1303-1310.

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In the social media, product reviews contain of text, emoticon, numbers and symbols that hard to identify the text summarization. Text analytics is one of the key techniques in exploring the unstructured data. The purpose of this study is solving the unstructured data by sort and summarizes the review data through a Web-Based Text Analytics using R approach. According to the comparative table between studies in Natural Language Processing (NLP) features, it was observed that Web-Based Text Analytics using R approach can analyze the unstructured data by using the data processing package in R. I

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Hau, Nguyen Phuc, Natalya E. Babushkina, and Said I. Eltaev. "PROCESSING UNSTRUCTURED DATA USING MACHINE LEARNING METHODS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 12/10, no. 153 (2024): 98–104. https://doi.org/10.36871/ek.up.p.r.2024.12.10.014.

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The article considers theoretical and practical aspects of unstructured data analysis using modern machine learning methods. It emphasizes the dominant position of unstructured information arrays in the global digital space and notes that traditional relational databases (RDBMS) are not suitable for working with such data. The article analyzes sources of unstructured information, including visual, text and multimedia formats, as well as the role of metadata and object storage in data management. Modern approaches based on computer vision and natural language processing (NLP) algorithms are con

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G., Bramhani, Bharathi M., Bhuvaneswari M., and Aditya Sai Srinivas T. "Pythonic Prose: A Journey into Text Analysis." Journal of Network Security and Data Mining 7, no. 2 (2024): 17–21. https://doi.org/10.5281/zenodo.10633663.

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<em>Text Analysis, which involves techniques like Text Mining and Natural Language Processing (NLP), stands as a transformative approach to extract valuable insights from unstructured text found in documents, emails, social media, and customer reviews. This article acts as a gateway to Text Analysis proficiency, with a primary focus on Python as the tool of choice. Serving as a comprehensive guide, it skilfully navigates readers through the intricacies of Text Analysis, providing them with the expertise to glean meaningful information from diverse textual sources. Tailored for those eager to e

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Osesina, O. Isaac, and John Talburt. "A Data-Intensive Approach to Named Entity Recognition Combining Contextual and Intrinsic Indicators." International Journal of Business Intelligence Research 3, no. 1 (2012): 55–71. http://dx.doi.org/10.4018/jbir.2012010104.

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Over the past decade, huge volumes of valuable information have become available to organizations. However, the existence of a substantial part of the information in unstructured form makes the automated extraction of business intelligence and decision support information from it difficult. By identifying the entities and their roles within unstructured text in a process known as semantic named entity recognition, unstructured text can be made more readily available for traditional business processes. The authors present a novel NER approach that is independent of the text language and subject

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Lin, Yao Hu, and Xue Lian Lin. "An Architecture for Unstructured Data Management." Advanced Materials Research 756-759 (September 2013): 1280–84. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1280.

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As the information age is coming, there is a vast amount of information available in the Internet. Most of data on Web are unstructured. But the significant data should be organized and stored in a suitable way for future purposes. One of the unsolved problems is the management of unstructured data. The unstructured data such as presentation, spreadsheet, text document, memo, images and web pages are difficult to manage while the data become a large scale and the users have different requirements and interests. In this paper, we proposed an architecture for unstructured data management by inte

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Lee, Gi-Eun, and Eun-Jun Park. "Research Trends Related on Hair Style in the Text Mining of Big Data Analysis." Journal of the Korean Society of Cosmetology 30, no. 3 (2024): 492–98. http://dx.doi.org/10.52660/jksc.2024.30.3.492.

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This study identified research trends by conducting keyword word frequency (TF), related word analysis (N-gram), and reverse document frequency (TF-IDF) through text mining, which analyzes text, which is unstructured data in big data, using the title and Korean abstract of domestic academic papers searched as hairstyles keywords in the Research Information Service (RISS). Through the research results, it was confirmed that hairstyles represent the characteristics of the times, and they are producing them or writing papers to understand preferences through statistics. The purpose of this study

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Ignaczak, Luciano, Guilherme Goldschmidt, Cristiano André Da Costa, and Rodrigo Da Rosa Righi. "Text Mining in Cybersecurity." ACM Computing Surveys 54, no. 7 (2021): 1–36. http://dx.doi.org/10.1145/3462477.

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The growth of data volume has changed cybersecurity activities, demanding a higher level of automation. In this new cybersecurity landscape, text mining emerged as an alternative to improve the efficiency of the activities involving unstructured data. This article proposes a Systematic Literature Review ( SLR ) to present the application of text mining in the cybersecurity domain. Using a systematic protocol, we identified 2,196 studies, out of which 83 were summarized. As a contribution, we propose a taxonomy to demonstrate the different activities in the cybersecurity domain supported by tex

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Gohourou, Didier, and Kazuhiro Kuwabara. "Knowledge Graph Extraction of Business Interactions from News Text for Business Networking Analysis." Machine Learning and Knowledge Extraction 6, no. 1 (2024): 126–42. http://dx.doi.org/10.3390/make6010007.

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Network representation of data is key to a variety of fields and their applications including trading and business. A major source of data that can be used to build insightful networks is the abundant amount of unstructured text data available through the web. The efforts to turn unstructured text data into a network have spawned different research endeavors, including the simplification of the process. This study presents the design and implementation of TraCER, a pipeline that turns unstructured text data into a graph, targeting the business networking domain. It describes the application of

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Michelson, M., and C. A. Knoblock. "Creating Relational Data from Unstructured and Ungrammatical Data Sources." Journal of Artificial Intelligence Research 31 (March 28, 2008): 543–90. http://dx.doi.org/10.1613/jair.2409.

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In order for agents to act on behalf of users, they will have to retrieve and integrate vast amounts of textual data on the World Wide Web. However, much of the useful data on the Web is neither grammatical nor formally structured, making querying difficult. Examples of these types of data sources are online classifieds like Craigslist and auction item listings like eBay. We call this unstructured, ungrammatical data "posts." The unstructured nature of posts makes query and integration difficult because the attributes are embedded within the text. Also, these attributes do not conform to stand

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Chuacharoen, Orathai, and Phannana Aiemsuwan. "Unstructured Data Management Model for Online Businesses in the New Normal Era." International Journal of Education and Literacy Studies 13, no. 2 (2025): 642–48. https://doi.org/10.7575/aiac.ijels.v.13n.2p.642.

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This research aims to study unstructured data management for online businesses and to develop an unstructured data management model for online businesses in the new normal era. This mixed-methods study surveyed unstructured data management practices among 400 social media users from platforms such as Facebook, Line, and Instagram, as well as five social media system administrators. The findings revealed that most social media users preferred storing images on devices used for viewing, searching, and purchasing products. Video and text data were primarily stored on devices such as external hard

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Kulkarni, Saurabh Shashikant. "Prediction and Analysis of Unstructured Text Data for Efficient Decision Making." International Journal for Research in Applied Science and Engineering Technology 7, no. 5 (2019): 554–58. http://dx.doi.org/10.22214/ijraset.2019.5092.

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Thakkar, Hiren Kumar, Priyanka Singh, and Yogesh Kumar. "DOMINER: Domain Feature Mining from Unstructured Data for Effective Text Summarization." Procedia Computer Science 235 (2024): 559–67. http://dx.doi.org/10.1016/j.procs.2024.04.055.

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