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Journal articles on the topic 'Methods of text mining'

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Published: 30 May 2022

Last updated: 31 May 2022

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1

VijayGaikwad, Sonali, Archana Chaugule, and Pramod Patil. "Text Mining Methods and Techniques." International Journal of Computer Applications 85, no. 17 (January 16, 2014): 42–45. http://dx.doi.org/10.5120/14937-3507.

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., D. M. Kulkarni. "USING DATA MINING METHODS KNOWLEDGE DISCOVERY FOR TEXT MINING." International Journal of Research in Engineering and Technology 03, no. 01 (January 25, 2014): 24–29. http://dx.doi.org/10.15623/ijret.2014.0301005.

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Solka, Jeffrey L. "Text Data Mining: Theory and Methods." Statistics Surveys 2 (2008): 94–112. http://dx.doi.org/10.1214/07-ss016.

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Dasri, Yugandhara Bapurao, Bhagyashree Vyankatrao Barde, Nalwade Prakash Shivajirao, and Anant Madhavrao Bainwad. "Text Mining Framework, Methods and Techniques." IOSR Journal of Computer Engineering 19, no. 04 (July 2017): 19–22. http://dx.doi.org/10.9790/0661-1904021922.

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Mansour, А. М., J. H. Mohammad, and Y. A. Kravchenko. "TEXT VECTORIZATION USING DATA MINING METHODS." IZVESTIYA SFedU. ENGINEERING SCIENCES, no. 2 (July 1, 2021): 154–67. http://dx.doi.org/10.18522/2311-3103-2021-2-154-167.

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Kohli, Monika, and Rohit Tiwari. "Survey on Data Mining Related Methods / Techniques and Text Mining." IJARCCE 7, no. 8 (August 30, 2018): 15–18. http://dx.doi.org/10.17148/ijarcce.2018.783.

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7

Natarajan, Jeyakumar. "Text Mining Perspectives in Microarray Data Mining." ISRN Computational Biology 2013 (November 5, 2013): 1–5. http://dx.doi.org/10.1155/2013/159135.

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Current microarray data mining methods such as clustering, classification, and association analysis heavily rely on statistical and machine learning algorithms for analysis of large sets of gene expression data. In recent years, there has been a growing interest in methods that attempt to discover patterns based on multiple but related data sources. Gene expression data and the corresponding literature data are one such example. This paper suggests a new approach to microarray data mining as a combination of text mining (TM) and information extraction (IE). TM is concerned with identifying patterns in natural language text and IE is concerned with locating specific entities, relations, and facts in text. The present paper surveys the state of the art of data mining methods for microarray data analysis. We show the limitations of current microarray data mining methods and outline how text mining could address these limitations.

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Carenini, Giuseppe, Gabriel Murray, and Raymond Ng. "Methods for Mining and Summarizing Text Conversations." Synthesis Lectures on Data Management 3, no. 3 (June 25, 2011): 1–130. http://dx.doi.org/10.2200/s00363ed1v01y201105dtm017.

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Cheng, Qijin, and Carrie S. M. Lui. "Applying text mining methods to suicide research." Suicide and Life-Threatening Behavior 51, no. 1 (February 2021): 137–47. http://dx.doi.org/10.1111/sltb.12680.

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Joorabchi, Arash, Michael English, and Abdulhussain E. Mahdi. "Text mining stackoverflow." Journal of Enterprise Information Management 29, no. 2 (March 7, 2016): 255–75. http://dx.doi.org/10.1108/jeim-11-2014-0109.

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Purpose – The use of social media and in particular community Question Answering (Q & A) websites by learners has increased significantly in recent years. The vast amounts of data posted on these sites provide an opportunity to investigate the topics under discussion and those receiving most attention. The purpose of this paper is to automatically analyse the content of a popular computer programming Q & A website, StackOverflow (SO), determine the exact topics of posted Q & As, and narrow down their categories to help determine subject difficulties of learners. By doing so, the authors have been able to rank identified topics and categories according to their frequencies, and therefore, mark the most asked about subjects and, hence, identify the most difficult and challenging topics commonly faced by learners of computer programming and software development. Design/methodology/approach – In this work the authors have adopted a heuristic research approach combined with a text mining approach to investigate the topics and categories of Q & A posts on the SO website. Almost 186,000 Q & A posts were analysed and their categories refined using Wikipedia as a crowd-sourced classification system. After identifying and counting the occurrence frequency of all the topics and categories, their semantic relationships were established. This data were then presented as a rich graph which could be visualized using graph visualization software such as Gephi. Findings – Reported results and corresponding discussion has given an indication that the insight gained from the process can be further refined and potentially used by instructors, teachers, and educators to pay more attention to and focus on the commonly occurring topics/subjects when designing their course material, delivery, and teaching methods. Research limitations/implications – The proposed approach limits the scope of the analysis to a subset of Q & As which contain one or more links to Wikipedia. Therefore, developing more sophisticated text mining methods capable of analysing a larger portion of available data would improve the accuracy and generalizability of the results. Originality/value – The application of text mining and data analytics technologies in education has created a new interdisciplinary field of research between the education and information sciences, called Educational Data Mining (EDM). The work presented in this paper falls under this field of research; and it is an early attempt at investigating the practical applications of text mining technologies in the area of computer science (CS) education.

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Papanikolaou, Nikolas, Georgios A. Pavlopoulos, Theodosios Theodosiou, and Ioannis Iliopoulos. "Protein–protein interaction predictions using text mining methods." Methods 74 (March 2015): 47–53. http://dx.doi.org/10.1016/j.ymeth.2014.10.026.

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Mashechkin, I. V., M. I. Petrovskiy, D. S. Popov, and D. V. Tsarev. "Applying text mining methods for data loss prevention." Programming and Computer Software 41, no. 1 (January 2015): 23–30. http://dx.doi.org/10.1134/s0361768815010041.

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Chen, Su Fen. "Redundant Feature Selection Methods in Text Classification." Advanced Materials Research 1044-1045 (October 2014): 1258–61. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1258.

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Feature selection is an effective pre-processing technology to facilitate text mining on high dimensional feature space. In recent years, many effective redundant feature selection methods have been proposed from different motivations. However, a comparative experimental study on redundant feature selection methods in the field of text mining has not been reported yet. In order to solve this problem, an extensive empirical comparative study with the task of text classification is given in the paper. The experimental results indicate that the 3-way Mutual Information represents the redundancy much better than traditional 2-way Mutual Information, since the label information are considered by 3-way Mutual Information. As a result, the performances of redundant feature selection methods based on 3-way Mutual Information overwhelm other methods.

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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 (November 7, 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 business processes. In this paper, we have highlighted the business values, some of the methods, and business application of text analytics.

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Lange, Christian, Maksim Abdul Latif, Yusuf Çelik, A. Melle Lyklema, Dafne E. van Kuppevelt, and Janneke van der Zwaan. "Text Mining Islamic Law." Islamic Law and Society 28, no. 3 (July 20, 2021): 234–81. http://dx.doi.org/10.1163/15685195-bja10009.

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Abstract Digital humanities has a venerable pedigree, stretching back to the middle of the twentieth century, but despite noteworthy pioneering contributions it has not become a mainstream practice in Islamic Studies. This essay applies humanities computing to the study of Islamic law. We analyze a representative corpus of works of Islamic substantive law (furūʿ al-fiqh) from the beginnings of Islamic legal jurisprudence to the early modern period (2nd/8th-13th/19th c.) using several computational tools and methods: text-reuse network analysis based on plain-text annotations and html tags, clustered frequency-based analysis, word clouds, and topic modeling. Applying machine-guided distant reading to Islamic legal texts over the longue-dureé, we study (1) the role of the Qurʾān, (2) patterns of normative qualifications (aḥkām), and (3) the distribution of topics in our corpus. In certain instances the analysis confirms claims made in the scholarly literature on Islamic law, in other instances it corrects such claims.

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Villarroel Ordenes, Francisco, and Shunyuan Zhang. "From words to pixels: text and image mining methods for service research." Journal of Service Management 30, no. 5 (November 29, 2019): 593–620. http://dx.doi.org/10.1108/josm-08-2019-0254.

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Purpose The purpose of this paper is to describe and position the state-of-the-art of text and image mining methods in business research. By providing a detailed conceptual and technical review of both methods, it aims to increase their utilization in service research. Design/methodology/approach On a first stage, the authors review business literature in marketing, operations and management concerning the use of text and image mining methods. On a second stage, the authors identify and analyze empirical papers that used text and image mining methods in services journals and premier business. Finally, avenues for further research in services are provided. Findings The manuscript identifies seven text mining methods and describes their approaches, processes, techniques and algorithms, involved in their implementation. Four of these methods are positioned similarly for image mining. There are 39 papers using text mining in service research, with a focus on measuring consumer sentiment, experiences, and service quality. Due to the nonexistent use of image mining service journals, the authors review their application in marketing and management, and suggest ideas for further research in services. Research limitations/implications This manuscript focuses on the different methods and their implementation in service research, but it does not offer a complete review of business literature using text and image mining methods. Practical implications The results have a number of implications for the discipline that are presented and discussed. The authors provide research directions using text and image mining methods in service priority areas such as artificial intelligence, frontline employees, transformative consumer research and customer experience. Originality/value The manuscript provides an introduction to text and image mining methods to service researchers and practitioners interested in the analysis of unstructured data. This paper provides several suggestions concerning the use of new sources of data (e.g. customer reviews, social media images, employee reviews and emails), measurement of new constructs (beyond sentiment and valence) and the use of more recent methods (e.g. deep learning).

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Abdessalem, Wahiba Karra Ben, and Soumaya Amdouni. "E-recruiting support system based on text mining methods." International Journal of Knowledge and Learning 7, no. 3/4 (2011): 220. http://dx.doi.org/10.1504/ijkl.2011.044542.

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DASRI, YUGANDHARA BAPURAO, and BHAGYASHREE VYANKATRAO BARDE. "A survey of text mining framework, methods and techniques." ENGINEERING AND TECHNOLOGY IN INDIA 8, no. 1and2 (October 15, 2017): 93–97. http://dx.doi.org/10.15740/has/eti/8.1and2/93-97.

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Reddy, Harita, Namratha Raj, Manali Gala, and Annappa Basava. "Text-mining-based Fake News Detection Using Ensemble Methods." International Journal of Automation and Computing 17, no. 2 (February 18, 2020): 210–21. http://dx.doi.org/10.1007/s11633-019-1216-5.

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20

R. Islam, Muhammad, I. F.T. Al-Shaikhli, and A. Abdulkadir. "A scientific review of soft-computing techniques and methods for stock market prediction." International Journal of Engineering & Technology 7, no. 2.5 (March 10, 2018): 27. http://dx.doi.org/10.14419/ijet.v7i2.5.10049.

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Information could be power if most technological approach engaged in the era of IT. Web based text mining is one of the approach that could be analyzed in many ways through soft-computing methods and techniques. The analytical result has shown that different methods of text mining have several advantages with the gap of knowledge that is required to improve. This paper explores the performance of various methods and its impact on specific text mining field such as web based financial text analysis for stock prediction. Key research area of financial text mining is becoming one of the potential research field based on the source of online news, forums, blogs or social media.

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Wang, Zhouhao, Enda Liu, Hiroki Sakaji, Tomoki Ito, Kiyoshi Izumi, Kota Tsubouchi, and Tatsuo Yamashita. "Estimation of Cross-Lingual News Similarities Using Text-Mining Methods." Journal of Risk and Financial Management 11, no. 1 (January 31, 2018): 8. http://dx.doi.org/10.3390/jrfm11010008.

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22

Süzen, Neslihan, Alexander N. Gorban, Jeremy Levesley, and Evgeny M. Mirkes. "Automatic short answer grading and feedback using text mining methods." Procedia Computer Science 169 (2020): 726–43. http://dx.doi.org/10.1016/j.procs.2020.02.171.

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Georgieva-Trifonova, Tsvetanka, and Miroslav Dechev. "Applying text mining methods to extracting information from news articles." IOP Conference Series: Materials Science and Engineering 1031, no. 1 (January 1, 2021): 012054. http://dx.doi.org/10.1088/1757-899x/1031/1/012054.

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Mohsen, Amr Mansour, Amira M. Idrees, and Hesham Ahmed Hassan. "Emotion Analysis for Opinion Mining From Text." International Journal of e-Collaboration 15, no. 1 (January 2019): 38–58. http://dx.doi.org/10.4018/ijec.2019010103.

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In the past few years, web documents, blogs, and reviews have played an important role in many fields as organizations always aim to find consumer or public opinions about their products and services. On the other hand, individual consumers also seek the opinions or emotions of existing users of a certain product before purchasing it. This method is currently one of the most vital methods for adapting the organizations' plans. In this article, the authors provide a survey for different techniques and approaches for emotion analysis from the text. They also demonstrate the techniques and the methods that have been proposed by different researchers with criticizing many of these methods according to the applied approach and the accuracy level.

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25

Noronha, Perpetua F., and Madhu Bhan. "Techniques and Issues in Text Mining." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4368–74. http://dx.doi.org/10.1166/jctn.2020.9079.

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Digital data in huge amount is being persistently generated at an unparalleled and exponential rate. In this digital era where internet stands the prime source for generating incredible information, it is vital to develop better means to mine the available information rapidly and most capably. Manual extraction of the salient information from the large input text documents is a time consuming and inefficient task. In this fast-moving world, it is difficult to read all the text-content and derive insights from it. Automatic methods are required. The task of probing for relevant documents from the large number of sources available, and consuming apt information from it is a challenging task and is need of the hour. Automatic text summarization technique can be used to generate relevant and quality information in less time. Text Summarization is used to condense the source text into a brief summary maintaining its salient information and readability. Generating summaries automatically is in great demand to attend to the growing and increasing amount of text data that is obtainable online in order to mark out the significant information and to consume it faster. Text summarization is becoming extremely popular with the advancement in Natural Language Processing (NLP) and deep learning methods. The most important gain of automatic text summarization is, it reduces the analysis time. In this paper we focus on key approaches to automatic text summarization and also about their efficiency and limitations.

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Vasilyev, Vladimir, Alexey Vulfin, and Nailya Kuchkarova. "Automation of Software Vulnerabilities Analysis on the Basis of Text Mining Technology." Voprosy kiberbezopasnosti, no. 4(38) (2020): 22–31. http://dx.doi.org/10.21681/2311-3456-2020-04-22-31.

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Purpose: the development of automated system of software vulnerabilities analysis for information-control systems on the basis of intelligent analysis of texts written on the natural language (Text Mining). Methods: the idea of the used investigation method is based on matching the set of extracted software vulnerabilities and relevant information security threats by means of evaluating the semantic similarity metrics of their textual description with use of Text Mining methods. Practical relevance: the architecture of the automated system of software vulnerabilities analysis is developed, the application of which allows us to evaluate the level of vulnerabilities criticality and match it with the most suitable by discretion (i.e. semantically similar) threats from the Bank of information security threats of FSTEC Russia while ensuring vulnerabilities and threats. The main software modules of the system have been developed. Computational experiments were carried out to assess the effectiveness of its application. The results of comparative analysis show that application of the given system allows us to increase the credibility of evaluating the criticality degree of vulnerabilities, considerably decreasing the time for a search and matching vulnerabilities and threats.

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Brindha, S., and Dr S. Sukumaran. "An Efficient Pharse Based Pattern Taxonomy Deploying Method for Text Document Mining." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1375–83. http://dx.doi.org/10.31142/ijtsrd11270.

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Hassani, Hossein, Christina Beneki, Stephan Unger, Maedeh Taj Mazinani, and Mohammad Reza Yeganegi. "Text Mining in Big Data Analytics." Big Data and Cognitive Computing 4, no. 1 (January 16, 2020): 1. http://dx.doi.org/10.3390/bdcc4010001.

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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Zeng, Zhiqiang, Hua Shi, Yun Wu, and Zhiling Hong. "Survey of Natural Language Processing Techniques in Bioinformatics." Computational and Mathematical Methods in Medicine 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/674296.

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Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.

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Shimodaira, Hiroyuki. "Application of Text Mining Methods to the History of Economic Thought:." History of Economic Thought 61, no. 1 (2019): 104–23. http://dx.doi.org/10.5362/jshet.61.1_104.

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Pandita, Rahul, Raoul Jetley, Sithu Sudarsan, Timothy Menzies, and Laurie Williams. "TMAP: Discovering relevant API methods through text mining of API documentation." Journal of Software: Evolution and Process 29, no. 12 (February 20, 2017): e1845. http://dx.doi.org/10.1002/smr.1845.

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Vazquez, Miguel, Martin Krallinger, Florian Leitner, and Alfonso Valencia. "Text Mining for Drugs and Chemical Compounds: Methods, Tools and Applications." Molecular Informatics 30, no. 6-7 (June 2011): 506–19. http://dx.doi.org/10.1002/minf.201100005.

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KM, Shivaprasad, and T. Hanumantha Reddy. "A Survey on Text Mining Techniques and Methods: A Review Approach." International Journal of Database Theory and Application 10, no. 1 (January 31, 2017): 11–22. http://dx.doi.org/10.14257/ijdta.2017.10.1.02.

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Singh Yadav, Ajit Kr, and Marpe Sora. "Fraud Detection in Financial Statements using Text Mining Methods: A Review." IOP Conference Series: Materials Science and Engineering 1020 (January 16, 2021): 012012. http://dx.doi.org/10.1088/1757-899x/1020/1/012012.

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viraja ravi, S., B. Dilip kumar, S. P. J. Gokhul Raj, and A. Divyacharan. "Text mining methods for online topics and reviews using machine learning." Journal of Physics: Conference Series 1916, no. 1 (May 1, 2021): 012215. http://dx.doi.org/10.1088/1742-6596/1916/1/012215.

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Jung, Hyun A., and Hyung Nam Kim. "An Analysis of Korean Dance Research Trends through Text Mining Methods." Journal of Dance Society for Documentation & History 62 (September 30, 2021): 91–123. http://dx.doi.org/10.26861/sddh.2021.62.91.

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Yu, Rong-Guo, Jia-Yu Zhang, Zhen-Tao Liu, You-Guang Zhuo, Hai-Yang Wang, Jie Ye, Nannan Liu, and Yi-Yuan Zhang. "Text Mining-Based Drug Discovery in Osteoarthritis." Journal of Healthcare Engineering 2021 (April 14, 2021): 1–14. http://dx.doi.org/10.1155/2021/6674744.

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Background. Osteoarthritis (OA) is a chronic and degenerative joint disease, which causes stiffness, pain, and decreased function. At the early stage of OA, nonsteroidal anti-inflammatory drugs (NSAIDs) are considered the first-line treatment. However, the efficacy and utility of available drug therapies are limited. We aim to use bioinformatics to identify potential genes and drugs associated with OA. Methods. The genes related to OA and NSAIDs therapy were determined by text mining. Then, the common genes were performed for GO, KEGG pathway analysis, and protein-protein interaction (PPI) network analysis. Using the MCODE plugin-obtained hub genes, the expression levels of hub genes were verified using quantitative real-time polymerase chain reaction (qRT-PCR). The confirmed genes were queried in the Drug Gene Interaction Database to determine potential genes and drugs. Results. The qRT-PCR result showed that the expression level of 15 genes was significantly increased in OA samples. Finally, eight potential genes were targetable to a total of 53 drugs, twenty-one of which have been employed to treat OA and 32 drugs have not yet been used in OA. Conclusions. The 15 genes (including PTGS2, NLRP3, MMP9, IL1RN, CCL2, TNF, IL10, CD40, IL6, NGF, TP53, RELA, BCL2L1, VEGFA, and NOTCH1) and 32 drugs, which have not been used in OA but approved by the FDA for other diseases, could be potential genes and drugs, respectively, to improve OA treatment. Additionally, those methods provided tremendous opportunities to facilitate drug repositioning efforts and study novel target pharmacology in the pharmaceutical industry.

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Islam, Mohammad Rabiul, Imad Fakhri Al-Shaikhli, Rizal Bin Mohd Nor, and Vijayakumar Varadarajan. "Technical Approach in Text Mining for Stock Market Prediction: A Systematic Review." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (May 1, 2018): 770. http://dx.doi.org/10.11591/ijeecs.v10.i2.pp770-777.

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Text mining methods and techniques have disclosed the mining task throughout information retrieval discipline in the field of soft computing techniques. To find the meaningful information from the vast amount of electronic textual data become a humongous task for trading decision. This empirical research of text mining role on financial text analysing in where stock predictive model need to improve based on rank search method. The review of this paper basically focused on text mining techniques, methods and principle component analysis that help reduce the dimensionality within the characteristics and optimal features. Moreover, most sophisticated soft-computing methods and techniques are reviewed in terms of analysis, comparison and evaluation for its performance based on electronic textual data. Due to research significance, this empirical research also highlights the limitation of different strategies and methods on exact aspects of theoretical framework for enhancing of performance.

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Jung, Kenneth, Paea LePendu, Srinivasan Iyer, Anna Bauer-Mehren, Bethany Percha, and Nigam H. Shah. "Functional evaluation of out-of-the-box text-mining tools for data-mining tasks." Journal of the American Medical Informatics Association 22, no. 1 (October 21, 2014): 121–31. http://dx.doi.org/10.1136/amiajnl-2014-002902.

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Abstract Objective The trade-off between the speed and simplicity of dictionary-based term recognition and the richer linguistic information provided by more advanced natural language processing (NLP) is an area of active discussion in clinical informatics. In this paper, we quantify this trade-off among text processing systems that make different trade-offs between speed and linguistic understanding. We tested both types of systems in three clinical research tasks: phase IV safety profiling of a drug, learning adverse drug–drug interactions, and learning used-to-treat relationships between drugs and indications. Materials We first benchmarked the accuracy of the NCBO Annotator and REVEAL in a manually annotated, publically available dataset from the 2008 i2b2 Obesity Challenge. We then applied the NCBO Annotator and REVEAL to 9 million clinical notes from the Stanford Translational Research Integrated Database Environment (STRIDE) and used the resulting data for three research tasks. Results There is no significant difference between using the NCBO Annotator and REVEAL in the results of the three research tasks when using large datasets. In one subtask, REVEAL achieved higher sensitivity with smaller datasets. Conclusions For a variety of tasks, employing simple term recognition methods instead of advanced NLP methods results in little or no impact on accuracy when using large datasets. Simpler dictionary-based methods have the advantage of scaling well to very large datasets. Promoting the use of simple, dictionary-based methods for population level analyses can advance adoption of NLP in practice.

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Zaki, Mohamed, and Janet R. McColl-Kennedy. "Text mining analysis roadmap (TMAR) for service research." Journal of Services Marketing 34, no. 1 (January 8, 2020): 30–47. http://dx.doi.org/10.1108/jsm-02-2019-0074.

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Purpose The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts. Design/methodology/approach The authors provide a six-stage TMAR on how to use text mining methods in practice. At each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts. Findings At each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice. Originality/value There is little research to guide scholars and practitioners on how to gain insights from the extensive “big data” that arises from the different data sources. In a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts.

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Percha, Bethany. "Modern Clinical Text Mining: A Guide and Review." Annual Review of Biomedical Data Science 4, no. 1 (July 20, 2021): 165–87. http://dx.doi.org/10.1146/annurev-biodatasci-030421-030931.

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Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g., physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, this review describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation in health systems and in industry.

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Kim, En-Gir, and Se-Hak Chun. "Analyzing Online Car Reviews Using Text Mining." Sustainability 11, no. 6 (March 17, 2019): 1611. http://dx.doi.org/10.3390/su11061611.

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Consumer reviews on the web have rapidly become an important information source through which consumers can share their experiences and opinions about products and services. It is a form of text-based communication that provides new possibilities and opens vast perspectives in terms of marketing. Reading consumer reviews gives marketers an opportunity to eavesdrop on their own consumers. This paper examines consumer reviews of three different competitive automobile brands and analyzes the advantages and disadvantages of each vehicle using text mining and association rule methods. The data were collected from an online resource for automotive information, Edmunds.com, with a scraping tool “ParseHub” and then processed in R software for statistical computing and graphics. The paper provides detailed insights into the superior and problematic sides of each brand and into consumers’ perceptions of automobiles and highlights differences between satisfied and unsatisfied groups regarding the best and worst features of the brands.

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Hathlian, Nourah F. Bin, and Alaaeldin M. Hafez. "Subjective Text Mining for Arabic Social Media." International Journal on Semantic Web and Information Systems 13, no. 2 (April 2017): 1–13. http://dx.doi.org/10.4018/ijswis.2017040101.

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The need for designing Arabic text mining systems for the use on social media posts is increasingly becoming a significant and attractive research area. It serves and enhances the knowledge needed in various domains. The main focus of this paper is to propose a novel framework combining sentiment analysis with subjective analysis on Arabic social media posts to determine whether people are interested or not interested in a defined subject. For those purposes, text classification methods—including preprocessing and machine learning mechanisms—are applied. Essentially, the performance of the framework is tested using Twitter as a data source, where possible volunteers on a certain subject are identified based on their posted tweets along with their subject-related information. Twitter is considered because of its popularity and its rich content from online microblogging services. The results obtained are very promising with an accuracy of 89%, thereby encouraging further research.

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Azeroual. "Text and Data Quality Mining in CRIS." Information 10, no. 12 (November 28, 2019): 374. http://dx.doi.org/10.3390/info10120374.

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To provide scientific institutions with comprehensive and well-maintained documentation of their research information in a current research information system (CRIS), they have the best prerequisites for the implementation of text and data mining (TDM) methods. Using TDM helps to better identify and eliminate errors, improve the process, develop the business, and make informed decisions. In addition, TDM increases understanding of the data and its context. This not only improves the quality of the data itself, but also the institution’s handling of the data and consequently the analyses. This present paper deploys TDM in CRIS to analyze, quantify, and correct the unstructured data and its quality issues. Bad data leads to increased costs or wrong decisions. Ensuring high data quality is an essential requirement when creating a CRIS project. User acceptance in a CRIS depends, among other things, on data quality. Not only is the objective data quality the decisive criterion, but also the subjective quality that the individual user assigns to the data.

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Ahadi, Alireza, Abhay Singh, Matt Bower, and Michael Garrett. "Text Mining in Education—A Bibliometrics-Based Systematic Review." Education Sciences 12, no. 3 (March 15, 2022): 210. http://dx.doi.org/10.3390/educsci12030210.

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Advances in Information Technology (IT) and computer science have without a doubt had a significant impact on our daily lives. The past few decades have witnessed the advancement of IT enabled processes in generating actionable insights in various fields, encouraging research based applications of modern Data Science methods. Among many other fields, education research has also been adopting different analytical approaches to advance the state of education systems. Moreover, developments in software engineering and web-based applications have made collection of education data possible at large scales. This systematic review aims to explore the 21st century’s state of the art applications of text mining methods used in the field of education. We analyse the metadata of all publications that use text mining or natural language processing in educational settings to report on the key themes of application of text mining methods in educational studies providing an overview of the current state of the art and the future directions for research and applications.

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Mazanec, Josef A. "Determining Long-Term Change in Tourism Research Language With Text-Mining Methods." Tourism Analysis 22, no. 1 (March 23, 2017): 75–83. http://dx.doi.org/10.3727/108354217x14828625279771.

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Liao, Xiyue, Guoqiang Chen, Ben Ku, Rahul Narula, and Janet Duncan. "Text Mining Methods Applied to Insurance Company Customer Calls: A Case Study." North American Actuarial Journal 24, no. 1 (November 1, 2019): 153–63. http://dx.doi.org/10.1080/10920277.2019.1649155.

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Béchet, Nicolas, Jacques Chauché, Violaine Prince, and Mathieu Roche. "How to combine text-mining methods to validate induced Verb-Object relations?" Computer Science and Information Systems 11, no. 1 (2014): 133–55. http://dx.doi.org/10.2298/csis130528021b.

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This paper describes methods using Natural Language Processing approaches to extract and validate induced syntactic relations (here restricted to the Verb-Object relation). These methods use a syntactic parser and a semantic closeness measure to extract such relations. Then, their validation is based on two different techniques: A Web Validation system on one part, then a Semantic-Vectorbased approach, and finally different combinations of both techniques in order to rank induced Verb-Object relations. The Semantic Vector approach is a Roget-based method which computes a syntactic relation as a vector. Web Validation uses a search engine to determine the relevance of a syntactic relation according to its popularity. An experimental protocol is set up to judge automatically the relevance of the sorted induced relations. We finally apply our approach on a French corpus of news by using ROC Curves to evaluate the results.

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Currie, Janet, Henrik Kleven, and Esmée Zwiers. "Technology and Big Data Are Changing Economics: Mining Text to Track Methods." AEA Papers and Proceedings 110 (May 1, 2020): 42–48. http://dx.doi.org/10.1257/pandp.20201058.

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The last 40 years have seen huge innovations in computing and in the availability of data. Data derived from millions of administrative records or by using (as we do) new methods of data generation such as text mining are now common. New data often requires new methods, which in turn can inspire new data collection. If history is any guide, some methods will stick and others will prove to be a flash in the pan. However, the larger trends toward demanding greater credibility and transparency from researchers in applied economics and a 'collage' approach to assembling evidence will likely continue.

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Chen, Xiaoyu, Jianjun Qi, Xiaomin Zhu, Xin Wang, and Zhen Wang. "Unlabelled text mining methods based on two extension models of concept lattices." International Journal of Machine Learning and Cybernetics 11, no. 2 (July 29, 2019): 475–90. http://dx.doi.org/10.1007/s13042-019-00987-6.

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