Relevant bibliographies by topics / Methods of text mining

Academic literature on the topic 'Methods of text mining'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Methods of text mining"

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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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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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Dissertations / Theses on the topic "Methods of text mining"

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Johnson, Eamon B. "Methods in Text Mining for Diagnostic Radiology." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1459514073.

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Eales, James Matthew. "Text-mining of experimental methods in phylogenetics." Thesis, University of Manchester, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.529251.

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Ashton, Triss A. "Accuracy and Interpretability Testing of Text Mining Methods." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc283791/.

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Extracting meaningful information from large collections of text data is problematic because of the sheer size of the database. However, automated analytic methods capable of processing such data have emerged. These methods, collectively called text mining first began to appear in 1988. A number of additional text mining methods quickly developed in independent research silos with each based on unique mathematical algorithms. How good each of these methods are at analyzing text is unclear. Method development typically evolves from some research silo centric requirement with the success of the method measured by a custom requirement-based metric. Results of the new method are then compared to another method that was similarly developed. The proposed research introduces an experimentally designed testing method to text mining that eliminates research silo bias and simultaneously evaluates methods from all of the major context-region text mining method families. The proposed research method follows a random block factorial design with two treatments consisting of three and five levels (RBF-35) with repeated measures. Contribution of the research is threefold. First, the users perceived a difference in the effectiveness of the various methods. Second, while still not clear, there are characteristics with in the text collection that affect the algorithms ability to extract meaningful results. Third, this research develops an experimental design process for testing the algorithms that is adaptable into other areas of software development and algorithm testing. This design eliminates the bias based practices historically employed by algorithm developers.
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Zakaria, Suliman Zubi. "Retrieving Electronic Data Interchange (EDI) Dataset using Text Mining Methods." Thesis, Сумський державний університет, 2012. http://essuir.sumdu.edu.ua/handle/123456789/28658.

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Abstract: - The internet is a huge source of documents, containing a massive number of texts presented in multilingual languages on a wide range of topics. These texts are demonstrating in an electronic documents format hosted on the web. The documents exchanged using special forms in an Electronic Data Interchange (EDI) environment. Using web text mining approaches to mine documents in EDI environment could be new challenging guidelines in web text mining. Applying text-mining approaches to discover knowledge previously unknown patters retrieved from the web documents by using partitioned cluster analysis methods such as k- means methods using Euclidean distance measure algorithm for EDI text document datasets is unique area of research these days. Our experiments employ the standard K-means algorithm on EDI text documents dataset that most commonly used in electronic interchange. We also report some results using text mining clustering application solution called WEKA. This study will provide high quality services to any organization that is willing to use the system. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/28658
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Bhattacharya, Sanmitra. "Computational methods for mining health communications in web 2.0." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/4576.

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Data from social media platforms are being actively mined for trends and patterns of interests. Problems such as sentiment analysis and prediction of election outcomes have become tremendously popular due to the unprecedented availability of social interactivity data of different types. In this thesis we address two problems that have been relatively unexplored. The first problem relates to mining beliefs, in particular health beliefs, and their surveillance using social media. The second problem relates to investigation of factors associated with engagement of U.S. Federal Health Agencies via Twitter and Facebook. In addressing the first problem we propose a novel computational framework for belief surveillance. This framework can be used for 1) surveillance of any given belief in the form of a probe, and 2) automatically harvesting health-related probes. We present our estimates of support, opposition and doubt for these probes some of which represent true information, in the sense that they are supported by scientific evidence, others represent false information and the remaining represent debatable propositions. We show for example that the levels of support in false and debatable probes are surprisingly high. We also study the scientific novelty of these probes and find that some of the harvested probes with sparse scientific evidence may indicate novel hypothesis. We also show the suitability of off-the-shelf classifiers for belief surveillance. We find these classifiers are quite generalizable and can be used for classifying newly harvested probes. Finally, we show the ability of harvesting and tracking probes over time. Although our work is focused in health care, the approach is broadly applicable to other domains as well. For the second problem, our specific goals are to study factors associated with the amount and duration of engagement of organizations. We use negative binomial hurdle regression models and Cox proportional hazards survival models for these. For Twitter, the hurdle analysis shows that presence of user-mention is positively associated with the amount of engagement while negative sentiment has inverse association. Content of tweets is also equally important for engagement. The survival analyses indicate that engagement duration is positively associated with follower count. For Facebook, both hurdle and survival analyses show that number of page likes and positive sentiment are correlated with higher and prolonged engagement while few content types are negatively correlated with engagement. We also find patterns of engagement that are consistent across Twitter and Facebook.
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Zhang, Xiaodan Hu Xiaohua. "Exploiting external/domain knowledge to enhance traditional text mining using graph-based methods /." Philadelphia, Pa. : Drexel University, 2009. http://hdl.handle.net/1860/3076.

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Davis, Aaron Samuel. "Bisecting Document Clustering Using Model-Based Methods." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1938.

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We all have access to large collections of digital text documents, which are useful only if we can make sense of them all and distill important information from them. Good document clustering algorithms that organize such information automatically in meaningful ways can make a difference in how effective we are at using that information. In this paper we use model-based document clustering algorithms as a base for bisecting methods in order to identify increasingly cohesive clusters from larger, more diverse clusters. We specifically use the EM algorithm and Gibbs Sampling on a mixture of multinomials as the base clustering algorithms on three data sets. Additionally, we apply a refinement step, using EM, to the final output of each clustering technique. Our results show improved agreement with human annotated document classes when compared to the existing base clustering algorithms, with marked improvement in two out of three data sets.
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Boynukalin, Zeynep. "Emotion Analysis Of Turkish Texts By Using Machine Learning Methods." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614521/index.pdf.

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Automatically analysing the emotion in texts is in increasing interest in today&rsquo
s research fields. The aim is to develop a machine that can detect type of user&rsquo
s emotion from his/her text. Emotion classification of English texts is studied by several researchers and promising results are achieved. In this thesis, an emotion classification study on Turkish texts is introduced. To the best of our knowledge, this is the first study on emotion analysis of Turkish texts. In English there exists some well-defined datasets for the purpose of emotion classification, but we could not find datasets in Turkish suitable for this study. Therefore, another important contribution is the generating a new data set in Turkish for emotion analysis. The dataset is generated by combining two types of sources. Several classification algorithms are applied on the dataset and results are compared. Due to the nature of Turkish language, new features are added to the existing methods to improve the success of the proposed method.
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Palma, Michael, and Shidi Zhou. "A Web Scraper For Forums : Navigation and text extraction methods." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219903.

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Web forums are a popular way of exchanging information and discussing various topics. These websites usually have a special structure, divided into boards, threads and posts. Although the structure might be consistent across forums, the layout of each forum is different. The way a web forum presents the user posts is also very different from how a news website presents a single piece of information. All of this makes the navigation and extraction of text a hard task for web scrapers. The focus of this thesis is the development of a web scraper specialized in forums. Three different methods for text extraction are implemented and tested before choosing the most appropriate method for the task. The methods are Word Count, Text-Detection Framework and Text-to-Tag Ratio. The handling of link duplicates is also considered and solved by implementing a multi-layer bloom filter. The thesis is conducted applying a qualitative methodology. The results indicate that the Text-to-Tag Ratio has the best overall performance and gives the most desirable result in web forums. Thus, this was the selected methods to keep on the final version of the web scraper.
Webforum är ett populärt sätt att utbyta information och diskutera olika ämnen. Dessa webbplatser har vanligtvis en särskild struktur, uppdelad i startsida, trådar och inlägg. Även om strukturen kan vara konsekvent bland olika forum är layouten av varje forum annorlunda. Det sätt på vilket ett webbforum presenterar användarinläggen är också väldigt annorlunda än hur en nyhet webbplats presenterar en enda informationsinlägg. Allt detta gör navigering och extrahering av text en svår uppgift för webbskrapor. Fokuset av detta examensarbete är utvecklingen av en webbskrapa specialiserad på forum. Tre olika metoder för textutvinning implementeras och testas innan man väljer den lämpligaste metoden för uppgiften. Metoderna är Word Count, Text Detection Framework och Text-to-Tag Ratio. Hanteringen av länk dubbleringar noga övervägd och löses genom att implementera ett flerlagers bloom filter. Examensarbetet genomförs med tillämpning av en kvalitativ metodik. Resultaten indikerar att Text-to-Tag Ratio har den bästa övergripande prestandan och ger det mest önskvärda resultatet i webbforum. Således var detta den valda metoden att behålla i den slutliga versionen av webbskrapan.
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Nhlabano, Valentine Velaphi. "Fast Data Analysis Methods For Social Media Data." Diss., University of Pretoria, 2018. http://hdl.handle.net/2263/72546.

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The advent of Web 2.0 technologies which supports the creation and publishing of various social media content in a collaborative and participatory way by all users in the form of user generated content and social networks has led to the creation of vast amounts of structured, semi-structured and unstructured data. The sudden rise of social media has led to their wide adoption by organisations of various sizes worldwide in order to take advantage of this new way of communication and engaging with their stakeholders in ways that was unimaginable before. Data generated from social media is highly unstructured, which makes it challenging for most organisations which are normally used for handling and analysing structured data from business transactions. The research reported in this dissertation was carried out to investigate fast and efficient methods available for retrieving, storing and analysing unstructured data form social media in order to make crucial and informed business decisions on time. Sentiment analysis was conducted on Twitter data called tweets. Twitter, which is one of the most widely adopted social network service provides an API (Application Programming Interface), for researchers and software developers to connect and collect public data sets of Twitter data from the Twitter database. A Twitter application was created and used to collect streams of real-time public data via a Twitter source provided by Apache Flume and efficiently storing this data in Hadoop File System (HDFS). Apache Flume is a distributed, reliable, and available system which is used to efficiently collect, aggregate and move large amounts of log data from many different sources to a centralized data store such as HDFS. Apache Hadoop is an open source software library that runs on low-cost commodity hardware and has the ability to store, manage and analyse large amounts of both structured and unstructured data quickly, reliably, and flexibly at low-cost. A Lexicon based sentiment analysis approach was taken and the AFINN-111 lexicon was used for scoring. The Twitter data was analysed from the HDFS using a Java MapReduce implementation. MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster. The results demonstrate that it is fast, efficient and economical to use this approach to analyse unstructured data from social media in real time.
Dissertation (MSc)--University of Pretoria, 2019.
National Research Foundation (NRF) - Scarce skills
Computer Science
MSc
Unrestricted
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Books on the topic "Methods of text mining"

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Text mining techniques for healthcare provider quality determination: Methods for rank comparisons. Hershey, PA: Medical Information Science Reference, 2010.

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Léon-Charles, Tranchevent, Moor Bart, Moreau Yves, and SpringerLink (Online service), eds. Kernel-based Data Fusion for Machine Learning: Methods and Applications in Bioinformatics and Text Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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Thomas S. Morton Grant S. Ingersoll. Taming Text: How to Find, Organize, and Manipulate It. [S.l.]: Manning Publications, 2012.

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Argamon, Shlomo. Computational methods for counterterrorism. Dordrecht: Springer, 2009.

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Berry, Michael W., and Jacob Kogan, eds. Text Mining. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470689646.

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Jo, Taeho. Text Mining. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91815-0.

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Biemann, Chris, and Alexander Mehler, eds. Text Mining. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12655-5.

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Weiss, Sholom M., Nitin Indurkhya, Tong Zhang, and Fred J. Damerau. Text Mining. New York, NY: Springer New York, 2005. http://dx.doi.org/10.1007/978-0-387-34555-0.

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ChengXiang, Zhai, and SpringerLink (Online service), eds. Mining Text Data. Boston, MA: Springer US, 2012.

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Dalianis, Hercules. Clinical Text Mining. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78503-5.

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Book chapters on the topic "Methods of text mining"

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Dalianis, Hercules. "Computational Methods for Text Analysis and Text Classification." In Clinical Text Mining, 83–96. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78503-5_8.

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Manderscheid, Katharina. "Text Mining." In Handbuch Methoden der empirischen Sozialforschung, 1103–16. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-21308-4_79.

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Zheng, Si, Shazia Dharssi, Meng Wu, Jiao Li, and Zhiyong Lu. "Text Mining for Drug Discovery." In Methods in Molecular Biology, 231–52. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9089-4_13.

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Kowald, Axel, and Sebastian Schmeier. "Text Mining for Systems Modeling." In Methods in Molecular Biology, 305–18. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-987-1_19.

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Maedche, Alexander, and Steffen Staab. "Mining Ontologies from Text." In Knowledge Engineering and Knowledge Management Methods, Models, and Tools, 189–202. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-39967-4_14.

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Tari, Luis B., and Jagruti H. Patel. "Systematic Drug Repurposing Through Text Mining." In Methods in Molecular Biology, 253–67. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0709-0_14.

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Wu, Heng-Yi, Chien-Wei Chiang, and Lang Li. "Text Mining for Drug–Drug Interaction." In Methods in Molecular Biology, 47–75. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0709-0_4.

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Howland, Peg, and Haesun Park. "Cluster-Preserving Dimension Reduction Methods for Efficient Classification of Text Data." In Survey of Text Mining, 3–23. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4305-0_1.

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Spinakis, Antonis, and Paraskevi Peristera. "Text Mining Tools: Evaluation Methods and Criteria." In Text Mining and its Applications, 131–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-45219-5_10.

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Triantaphyllou, Evangelos. "Data Mining of Text Documents." In Data Mining and Knowledge Discovery via Logic-Based Methods, 257–76. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1630-3_13.

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Conference papers on the topic "Methods of text mining"

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Bhonde, S. B., R. L. Paikrao, K. U. Rahane, R. B. Patel, and B. P. Singh. "Text Association Analysis and Ambiguity in Text Mining." In INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN SCIENCE AND TECHNOLOGY (ICM2ST-10). AIP, 2010. http://dx.doi.org/10.1063/1.3526195.

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Carenini, Giuseppe, and Gabrial Murray. "Methods for mining and summarizing text conversations." In the 35th international ACM SIGIR conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2348283.2348529.

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Consoli, Domenico, George Maroulis, and Theodore E. Simos. "Analysing Customer Opinions with Text Mining Algorithms." In COMPUTATIONAL METHODS IN SCIENCE AND ENGINEERING: Advances in Computational Science: Lectures presented at the International Conference on Computational Methods in Sciences and Engineering 2008 (ICCMSE 2008). AIP, 2009. http://dx.doi.org/10.1063/1.3225451.

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Lee, Sangno, Jeff Baker, Jaeki Song, and James C. Wetherbe. "An Empirical Comparison of Four Text Mining Methods." In 2010 43rd Hawaii International Conference on System Sciences. IEEE, 2010. http://dx.doi.org/10.1109/hicss.2010.48.

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Hatipoglu, Poyraz Umut, Anil Atvar, Yusuf Oguzhan Artan, Oguzhan Sereflisan, and Ali Demir. "Software requirement traceability analysis using text mining methods." In 2017 25th Signal Processing and Communications Applications Conference (SIU). IEEE, 2017. http://dx.doi.org/10.1109/siu.2017.7960424.

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Abdallah, Ziad, Ali El-Zaart, and Mohamad Oueidat. "Comparison of multilabel problem transformation methods for text mining." In 2015 Fifth International Conference on Digital Information and Communication Technology and its Applications (DICTAP). IEEE, 2015. http://dx.doi.org/10.1109/dictap.2015.7113182.

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Kulkarni, D. S., and S. F. Rodd. "Extensive study of text based methods for opinion mining." In 2018 2nd International Conference on Inventive Systems and Control (ICISC). IEEE, 2018. http://dx.doi.org/10.1109/icisc.2018.8399127.

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Alksher, Mostafa A., Azreen Azman, Razali Yaakob, Rabiah Abdul Kadir, Abdulmajid Mohamed, and Eissa M. Alshari. "A review of methods for mining idea from text." In 2016 Third International Conference on Information Retrieval and Knowledge Management (CAMP). IEEE, 2016. http://dx.doi.org/10.1109/infrkm.2016.7806341.

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Lee, Doheon. "Session details: Methods for Bio-data and Text Mining." In CIKM'15: 24th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/3252352.

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Feng, Lingyun, Minghui Qiu, Yaliang Li, Haitao Zheng, and Ying Shen. "Wasserstein Selective Transfer Learning for Cross-domain Text Mining." In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.emnlp-main.770.

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Reports on the topic "Methods of text mining"

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Currie, Janet, Henrik Kleven, and Esmée Zwiers. Technology and Big Data Are Changing Economics: Mining Text to Track Methods. Cambridge, MA: National Bureau of Economic Research, January 2020. http://dx.doi.org/10.3386/w26715.

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Alexandrov, Boian, and Maksim Eren. Tensor Text-Mining Methods for Malware Identification and Detection, Malware Dynamics Characterization, and Hosts Ranking. Office of Scientific and Technical Information (OSTI), October 2021. http://dx.doi.org/10.2172/1826495.

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Paynter, Robin A., Celia Fiordalisi, Elizabeth Stoeger, Eileen Erinoff, Robin Featherstone, Christiane Voisin, and Gaelen P. Adam. A Prospective Comparison of Evidence Synthesis Search Strategies Developed With and Without Text-Mining Tools. Agency for Healthcare Research and Quality (AHRQ), March 2021. http://dx.doi.org/10.23970/ahrqepcmethodsprospectivecomparison.

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Background: In an era of explosive growth in biomedical evidence, improving systematic review (SR) search processes is increasingly critical. Text-mining tools (TMTs) are a potentially powerful resource to improve and streamline search strategy development. Two types of TMTs are especially of interest to searchers: word frequency (useful for identifying most used keyword terms, e.g., PubReminer) and clustering (visualizing common themes, e.g., Carrot2). Objectives: The objectives of this study were to compare the benefits and trade-offs of searches with and without the use of TMTs for evidence synthesis products in real world settings. Specific questions included: (1) Do TMTs decrease the time spent developing search strategies? (2) How do TMTs affect the sensitivity and yield of searches? (3) Do TMTs identify groups of records that can be safely excluded in the search evaluation step? (4) Does the complexity of a systematic review topic affect TMT performance? In addition to quantitative data, we collected librarians' comments on their experiences using TMTs to explore when and how these new tools may be useful in systematic review search¬¬ creation. Methods: In this prospective comparative study, we included seven SR projects, and classified them into simple or complex topics. The project librarian used conventional “usual practice” (UP) methods to create the MEDLINE search strategy, while a paired TMT librarian simultaneously and independently created a search strategy using a variety of TMTs. TMT librarians could choose one or more freely available TMTs per category from a pre-selected list in each of three categories: (1) keyword/phrase tools: AntConc, PubReMiner; (2) subject term tools: MeSH on Demand, PubReMiner, Yale MeSH Analyzer; and (3) strategy evaluation tools: Carrot2, VOSviewer. We collected results from both MEDLINE searches (with and without TMTs), coded every citation’s origin (UP or TMT respectively), deduplicated them, and then sent the citation library to the review team for screening. When the draft report was submitted, we used the final list of included citations to calculate the sensitivity, precision, and number-needed-to-read for each search (with and without TMTs). Separately, we tracked the time spent on various aspects of search creation by each librarian. Simple and complex topics were analyzed separately to provide insight into whether TMTs could be more useful for one type of topic or another. Results: Across all reviews, UP searches seemed to perform better than TMT, but because of the small sample size, none of these differences was statistically significant. UP searches were slightly more sensitive (92% [95% confidence intervals (CI) 85–99%]) than TMT searches (84.9% [95% CI 74.4–95.4%]). The mean number-needed-to-read was 83 (SD 34) for UP and 90 (SD 68) for TMT. Keyword and subject term development using TMTs generally took less time than those developed using UP alone. The average total time was 12 hours (SD 8) to create a complete search strategy by UP librarians, and 5 hours (SD 2) for the TMT librarians. TMTs neither affected search evaluation time nor improved identification of exclusion concepts (irrelevant records) that can be safely removed from the search set. Conclusion: Across all reviews but one, TMT searches were less sensitive than UP searches. For simple SR topics (i.e., single indication–single drug), TMT searches were slightly less sensitive, but reduced time spent in search design. For complex SR topics (e.g., multicomponent interventions), TMT searches were less sensitive than UP searches; nevertheless, in complex reviews, they identified unique eligible citations not found by the UP searches. TMT searches also reduced time spent in search strategy development. For all evidence synthesis types, TMT searches may be more efficient in reviews where comprehensiveness is not paramount, or as an adjunct to UP for evidence syntheses, because they can identify unique includable citations. If TMTs were easier to learn and use, their utility would be increased.
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Gates, Allison, Michelle Gates, Shannon Sim, Sarah A. Elliott, Jennifer Pillay, and Lisa Hartling. Creating Efficiencies in the Extraction of Data From Randomized Trials: A Prospective Evaluation of a Machine Learning and Text Mining Tool. Agency for Healthcare Research and Quality (AHRQ), August 2021. http://dx.doi.org/10.23970/ahrqepcmethodscreatingefficiencies.

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Background. Machine learning tools that semi-automate data extraction may create efficiencies in systematic review production. We prospectively evaluated an online machine learning and text mining tool’s ability to (a) automatically extract data elements from randomized trials, and (b) save time compared with manual extraction and verification. Methods. For 75 randomized trials published in 2017, we manually extracted and verified data for 21 unique data elements. We uploaded the randomized trials to ExaCT, an online machine learning and text mining tool, and quantified performance by evaluating the tool’s ability to identify the reporting of data elements (reported or not reported), and the relevance of the extracted sentences, fragments, and overall solutions. For each randomized trial, we measured the time to complete manual extraction and verification, and to review and amend the data extracted by ExaCT (simulating semi-automated data extraction). We summarized the relevance of the extractions for each data element using counts and proportions, and calculated the median and interquartile range (IQR) across data elements. We calculated the median (IQR) time for manual and semiautomated data extraction, and overall time savings. Results. The tool identified the reporting (reported or not reported) of data elements with median (IQR) 91 percent (75% to 99%) accuracy. Performance was perfect for four data elements: eligibility criteria, enrolment end date, control arm, and primary outcome(s). Among the top five sentences for each data element at least one sentence was relevant in a median (IQR) 88 percent (83% to 99%) of cases. Performance was perfect for four data elements: funding number, registration number, enrolment start date, and route of administration. Among a median (IQR) 90 percent (86% to 96%) of relevant sentences, pertinent fragments had been highlighted by the system; exact matches were unreliable (median (IQR) 52 percent [32% to 73%]). A median 48 percent of solutions were fully correct, but performance varied greatly across data elements (IQR 21% to 71%). Using ExaCT to assist the first reviewer resulted in a modest time savings compared with manual extraction by a single reviewer (17.9 vs. 21.6 hours total extraction time across 75 randomized trials). Conclusions. Using ExaCT to assist with data extraction resulted in modest gains in efficiency compared with manual extraction. The tool was reliable for identifying the reporting of most data elements. The tool’s ability to identify at least one relevant sentence and highlight pertinent fragments was generally good, but changes to sentence selection and/or highlighting were often required.
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Kostoff, Ronald N., Henry A. Buchtel, John Andrews, and Kirstin M. Pfeil. Science and Technology Text Mining: Text Mining of the Journal Cortex. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada425249.

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Kostoff, Ronald N., and James Hartley. Science and Technology Text Mining: Structured Papers. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada417220.

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Kostoff, Ronald N., and Ronald A. DeMarco. Science and Technology Text Mining: Analytical Chemistry. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada415945.

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Kostoff, Ronald N., Michael F. Shlesinger, and Rene Tshiteya. Science and Technology Text Mining: Nonlinear Dynamics. Fort Belvoir, VA: Defense Technical Information Center, February 2004. http://dx.doi.org/10.21236/ada420998.

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Kostoff, Ronald N., Rene Tshiteya, Jesse Stump, Guido Malpohl, and George Karypis. Science and Technology Text Mining: Wireless LANS. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada437247.

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Kostoff, Ronald N., J. A. del Rio, Esther O. Garcia, Ana M. Ramirez, and James A. Humenik. Science and Technology Text Mining: Citation Mining of Dynamic Granular Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada418862.

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