Uit de aankondiging: "Steeds meer systemen loggen gegevens over hoe het bedrijfsproces verloopt, maar loopt het proces wel zoals het bedoeld was? Wat zijn de knelpunten? Text mining is vaak lastig doordat er tijdstippen ontbreken, process mining kan niet werken zonder tijdstippen, de combinatie van die twee technieken kan elkaar versterken. Bij sentiment mining weet je wel wat iemands zijn gevoelens zijn, maar niet zijn drijfveren, terwijl drijfveren juist een betere verklaring voor iemands gedrag vormen. De combinatie van deze technieken biedt mogelijkheden om nieuwe inzichten te verwerven rond customer journeys, zodat de klant uiteindelijk beter geholpen wordt." http://www.naf.nl/events/proces-text-mining/
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This method paper presents a template solution for text mining of scientific literature using the R tm package. Literature to be analyzed can be collected manually or automatically using the code provided with this paper. Once the literature is collected, the three steps for conducting text mining can be performed as outlined below:• loading and cleaning of text from articles,• processing, statistical analysis, and clustering, and• presentation of results using generalized and tailor-made visualizations.The text mining steps can be applied to a single, multiple, or time series groups of documents.References are provided to three published peer reviewed articles that use the presented text mining methodology. The main advantages of our method are: (1) Its suitability for both research and educational purposes, (2) Compliance with the Findable Accessible Interoperable and Reproducible (FAIR) principles, and (3) code and example data are made available on GitHub under the open-source Apache V2 license.
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In this article, we present CoPub 5.0, a publicly available text mining system, which uses Medline abstracts to calculate robust statistics for keyword co-occurrences. CoPub was initially developed for the analysis of microarray data, but we broadened the scope by implementing new technology and new thesauri. In CoPub 5.0, we integrated existing CoPub technology with new features, and provided a new advanced interface, which can be used to answer a variety of biological questions. CoPub 5.0 allows searching for keywords of interest and its relations to curated thesauri and provides highlighting and sorting mechanisms, using its statistics, to retrieve the most important abstracts in which the terms co-occur. It also provides a way to search for indirect relations between genes, drugs, pathways and diseases, following an ABC principle, in which A and C have no direct connection but are connected via shared B intermediates. With CoPub 5.0, it is possible to create, annotate and analyze networks using the layout and highlight options of Cytoscape web, allowing for literature based systems biology. Finally, operations of the CoPub 5.0 Web service enable to implement the CoPub technology in bioinformatics workflows. CoPub 5.0 can be accessed through the CoPub portal http://www.copub.org. © 2011 The Author(s).
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Objective: To annotate a corpus of randomized controlled trial (RCT) publications with the checklist items of CONSORT reporting guidelines and using the corpus to develop text mining methods for RCT appraisal. Methods: We annotated a corpus of 50 RCT articles at the sentence level using 37 fine-grained CONSORT checklist items. A subset (31 articles) was double-annotated and adjudicated, while 19 were annotated by a single annotator and reconciled by another. We calculated inter-annotator agreement at the article and section level using MASI (Measuring Agreement on Set-Valued Items) and at the CONSORT item level using Krippendorff's α. We experimented with two rule-based methods (phrase-based and section header-based) and two supervised learning approaches (support vector machine and BioBERT-based neural network classifiers), for recognizing 17 methodology-related items in the RCT Methods sections. Results: We created CONSORT-TM consisting of 10,709 sentences, 4,845 (45%) of which were annotated with 5,246 labels. A median of 28 CONSORT items (out of possible 37) were annotated per article. Agreement was moderate at the article and section levels (average MASI: 0.60 and 0.64, respectively). Agreement varied considerably among individual checklist items (Krippendorff's α= 0.06–0.96). The model based on BioBERT performed best overall for recognizing methodology-related items (micro-precision: 0.82, micro-recall: 0.63, micro-F1: 0.71). Combining models using majority vote and label aggregation further improved precision and recall, respectively. Conclusion: Our annotated corpus, CONSORT-TM, contains more fine-grained information than earlier RCT corpora. Low frequency of some CONSORT items made it difficult to train effective text mining models to recognize them. For the items commonly reported, CONSORT-TM can serve as a testbed for text mining methods that assess RCT transparency, rigor, and reliability, and support methods for peer review and authoring assistance. Minor modifications to the annotation scheme and a larger corpus could facilitate improved text mining models. CONSORT-TM is publicly available at https://github.com/kilicogluh/CONSORT-TM.
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handout van een labtalk waarin de onderzoeker enkele methoden beschrijft rond text mining, story mining: het herkennen van patronen in communicatie met klanten.
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E-discovery projects typically start with an assessment of the collected electronic data in order to estimate the risk to prosecute or defend a legal case. This is not a review task but is appropriately called early case assessment, which is better known as exploratory search in the information retrieval community. This paper first describes text mining methodologies that can be used for enhancing exploratory search. Based on these ideas we present a semantic search dashboard that includes entities that are relevant to investigators such as who knew who, what, where and when. We describe how this dashboard can be powered by results from our ongoing research in the “Semantic Search for E-Discovery” project on topic detection and clustering, semantic enrichment of user profiles, email recipient recommendation, expert finding and identity extraction from digital forensic evidence.
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The main goal of this study was to investigate if a computational analyses of text data from the National Student Survey (NSS) can add value to the existing, manual analysis. The results showed the computational analysis of the texts from the open questions of the NSS contain information which enriches the results of standard quantitative analysis of the NSS.
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Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework’s efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.
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The goal of this study was therefore to test the idea that computationally analysing the Fontys National Student Surveys (NSS) open answers using a selection of standard text mining methods (Manning & Schütze 1999) will increase the value of these answers for educational quality assurance. It is expected that human effort and time of analysis will decrease significally. The text data (in Dutch) of several years of Fontys National Student Surveys (2013-2018) was provided to Fontys students of the minor Applied Data Science. The results of the analysis were to include topic and sentiment modelling across multiple years of survey data. Comparing multiple years was necessary to capture and visualize any trends that a human investigator may have missed while analysing the data by hand. During data cleaning all stop words and punctuation were removed, all text was brought to a lower case, names and inappropriate language – such as swear words – were deleted. About 80% of 24.000 records were manually labelled with sentiment; reminder was used for algorithms’ validation. In the following step a machine learning analysis steps: training, testing, outcomes analysis and visualisation, for a better text comprehension, were executed. Students aimed to improve classification accuracy by applying multiple sentiment analysis algorithms and topics modelling methods. The models were chosen arbitrarily, with a preference for a low complexity of a model. For reproducibility of our study open source tooling was used. One of these tools was based on Latent Dirichlet allocation (LDA). LDA is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar (Blei, Ng & Jordan, 2003). For topic modelling the Gensim (Řehůřek, 2011) method was used. Gensim is an open-source vector space modelling and topic modelling toolkit implemented in Python. In addition, we recognized the absence of pretrained models for Dutch language. To complete our prototype a simple user interface was created in Python. This final step integrated our automated text analysis with visualisations of sentiments and topics. Remarkably, all extracted topics are related to themes defined by the NSS. This indicates that in general students’ answers are related to topics of interest for educational institutions. The extracted list of the words related to the topic is also relevant to this topic. Despite the fact that most of the results require further human expert interpretation, it is indicative to conclude that the computational analysis of the texts from the open questions of the NSS contain information which enriches the results of standard quantitative analysis of the NSS.
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This study furthers game-based learning for circular business model innovation (CBMI), the complex, dynamic process of designing business models according to the circular economy principles. The study explores how game-play in an educational setting affects learning progress on the level of business model elements and from the perspective of six learning categories. We experimented with two student groups using our game education package Re-Organise. All students first studied a reader and a game role description and then filled out a circular business model canvas and a learning reflection. The first group, i.e., the game group, updated the canvas and the reflection in an interactive tutorial after gameplay. The control group submitted their updated canvas and reflection directly after the interactive tutorial without playing the game. The results were analyzed using text-mining and qualitative methods such as word co-occurrence and sentiment polarity. The game group created richer business models (using more waste processing technologies) and reflections with stronger sentiments toward the learning experience. Our detailed study results (i.e., per business model element and learning category) enhance understanding of game-based learning for circular business model innovation while providing directions for improving serious games and accompanying educational packages.
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