Unsupervised document classification integrating web scraping, one-class SVM and LDA topic modelling.
Ontology highlight
ABSTRACT:
SUBMITTER: Thielmann A
PROVIDER: S-EPMC9930816 | biostudies-literature | 2023
REPOSITORIES: biostudies-literature
Publications
Unsupervised document classification integrating web scraping, one-class SVM and LDA topic modelling.
Thielmann Anton A Weisser Christoph C Krenz Astrid A Säfken Benjamin B
Journal of applied statistics 20210427 3
Unsupervised document classification for imbalanced data sets poses a major challenge. To obtain accurate classification results, training data sets are often created manually by humans which requires expert knowledge, time and money. Depending on the imbalance of the data set, this approach also either requires human labelling of all of the data or it fails to adequately recognize underrepresented categories. We propose an integration of web scraping, one-class Support Vector Machines (SVM) and ...[more]
Similar Datasets
Project description:An immense volume of digital documents exists online and offline with content that can offer useful information and insights. Utilizing topic modeling enhances the analysis and understanding of digital documents. Topic modeling discovers latent semantic structures or topics within a set of digital textual documents. The Internet of Things, Blockchain, recommender system, and search engine optimization applications use topic modeling to handle data mining tasks, such as classification and clustering. The usefulness of topic models depends on the quality of resulting term patterns and topics with high quality. Topic coherence is the standard metric to measure the quality of topic models. Previous studies build topic models to generally work on conventional documents, and they are insufficient and underperform when applied to web content data due to differences in the structure of the conventional and HTML documents. Neglecting the unique structure of web content leads to missing otherwise coherent topics and, therefore, low topic quality. This study aims to propose an innovative topic model to learn coherence topics in web content data. We present the HTML Topic Model (HTM), a web content topic model that takes into consideration the HTML tags to understand the structure of web pages. We conducted two series of experiments to demonstrate the limitations of the existing topic models and examine the topic coherence of the HTM against the widely used Latent Dirichlet Allocation (LDA) model and its variants, namely the Correlated Topic Model, the Dirichlet Multinomial Regression, the Hierarchical Dirichlet Process, the Hierarchical Latent Dirichlet Allocation, the pseudo-document based Topic Model, and the Supervised Latent Dirichlet Allocation models. The first experiment demonstrates the limitations of the existing topic models when applied to web content data and, therefore, the essential need for a web content topic model. When applied to web data, the overall performance dropped an average of five times and, in some cases, up to approximately 20 times lower than when applied to conventional data. The second experiment then evaluates the effectiveness of the HTM model in discovering topics and term patterns of web content data. The HTM model achieved an overall 35% improvement in topic coherence compared to the LDA.
| S-EPMC10403181 | biostudies-literature
Project description:MotivationWe recently published MS2LDA, a method for the decomposition of sets of molecular fragment data derived from large metabolomics experiments. To make the method more widely available to the community, here we present ms2lda.org, a web application that allows users to upload their data, run MS2LDA analyses and explore the results through interactive visualizations.ResultsMs2lda.org takes tandem mass spectrometry data in many standard formats and allows the user to infer the sets of fragment and neutral loss features that co-occur together (Mass2Motifs). As an alternative workflow, the user can also decompose a data set onto predefined Mass2Motifs. This is accomplished through the web interface or programmatically from our web service.Availability and implementationThe website can be found at http://ms2lda.org, while the source code is available at https://github.com/sdrogers/ms2ldaviz under the MIT license.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC5860206 | biostudies-literature
Project description:IntroductionAt present, machine learning and image processing technology are widely used in plant disease diagnosis. In order to address the challenges of subjectivity, cost, and timeliness associated with traditional methods of diagnosing potassium deficiency in apple tree leaves.MethodsThe study proposes a model that utilizes image processing technology and machine learning techniques to enhance the accuracy of detection during each growth period. Leaf images were collected at different growth stages and processed through denoising and segmentation. Color and shape features of the leaves were extracted and a multiple regression analysis model was used to screen for key features. Linear discriminant analysis was then employed to optimize the data and obtain the optimal shape and color feature factors of apple tree leaves during each growth period. Various machine-learning methods, including SVM, DT, and KNN, were used for the diagnosis of potassium deficiency.ResultsThe MLR-LDA-SVM model was found to be the optimal model based on comprehensive evaluation indicators. Field experiments were conducted to verify the accuracy of the diagnostic model, achieving high diagnostic accuracy during different growth periods.DiscussionThe model can accurately diagnose whether potassium deficiency exists in apple tree leaves during each growth period. This provides theoretical guidance for intelligent and precise water and fertilizer management in orchards.
| S-EPMC10716244 | biostudies-literature
Project description:It has become increasingly necessary to automate systems for organizing and classifying user reviews according to their domain-specific aspects and sentiment polarities, as online customer opinions have increased on specialized platforms and social networks. This study's methodology employs a combination of topic modeling and sentiment analysis, as well as human validation techniques of topic labels, to extract valuable insights about Marrakech city from TripAdvisor reviews. Through this technique, tourism practitioners and field specialists may dive deeper into online users generated content, leveraging aspect-based sentiment analysis to explore each destination's weaknesses and strengths.•Data collection and pre-processing.•Extracting latent topics using LDA algorithm (Latent Dirichlet Allocation) on collected reviews.•Applying sentiment analysis to each topic.
| S-EPMC9672446 | biostudies-literature
Project description:This paper presents an off-topic detection algorithm combining LDA and word2vec aiming at the problem of the lack of accurate and efficient off-topic detection algorithms in the English composition-assisted review system. The algorithm uses the LDA model to model the document and train the document through the word2vec, and uses the semantic relationship between the document's topics and words to calculate the probability weighted sum for each topic and its feature words in the document, and finally selects the off-topic composition by setting a reasonable threshold. Different F values are obtained by changing the number of topics in the document, and the best number of topics is determined. Experimental results show that the proposed method is more effective than vector space model, can detect more off-topic compositions, and the accuracy is higher, the F value is more than 88%, which realizes the intelligent processing of off-topic detection of composition, and can be effectively applied in English composition teaching.
| S-EPMC8880936 | biostudies-literature
Project description:BackgroundThe importance of systematic reviews in collating and summarising available research output on a particular topic cannot be over-emphasized. However, initial screening of retrieved literature is significantly time and labour intensive. Attempts at automating parts of the systematic review process have been made with varying degree of success partly due to being domain-specific, requiring vendor-specific software or manually labelled training data. Our primary objective was to develop statistical methodology for performing automated title and abstract screening for systematic reviews. Secondary objectives included (1) to retrospectively apply the automated screening methodology to previously manually screened systematic reviews and (2) to characterize the performance of the automated screening methodology scoring algorithm in a simulation study.MethodsWe implemented a Latent Dirichlet Allocation-based topic model to derive representative topics from the retrieved documents' title and abstract. The second step involves defining a score threshold for classifying the documents as relevant for full-text review or not. The score is derived based on a set of search keywords (often the database retrieval search terms). Two systematic review studies were retrospectively used to illustrate the methodology.ResultsIn one case study (helminth dataset), [Formula: see text] sensitivity compared to manual title and abstract screening was achieved. This is against a false positive rate of [Formula: see text]. For the second case study (Wilson disease dataset), a sensitivity of [Formula: see text] and specificity of [Formula: see text] were achieved.ConclusionsUnsupervised title and abstract screening has the potential to reduce the workload involved in conducting systematic review. While sensitivity of the methodology on the tested data is low, approximately [Formula: see text] specificity was achieved. Users ought to keep in mind that potentially low sensitivity might occur. One approach to mitigate this might be to incorporate additional targeted search keywords such as the indexing databases terms into the search term copora. Moreover, automated screening can be used as an additional screener to the manual screeners.
| S-EPMC9811792 | biostudies-literature
Project description:A key goal of disease surveillance is to identify outbreaks of known or novel diseases in a timely manner. Such an outbreak occurred in the UK associated with acute vomiting in dogs between December 2019 and March 2020. We tracked this outbreak using the clinical free text component of anonymised electronic health records (EHRs) collected from a sentinel network of participating veterinary practices. We sourced the free text (narrative) component of each EHR supplemented with one of 10 practitioner-derived main presenting complaints (MPCs), with the 'gastroenteric' MPC identifying cases involved in the disease outbreak. Such clinician-derived annotation systems can suffer from poor compliance requiring retrospective, often manual, coding, thereby limiting real-time usability, especially where an outbreak of a novel disease might not present clinically as a currently recognised syndrome or MPC. Here, we investigate the use of an unsupervised method of EHR annotation using latent Dirichlet allocation topic-modelling to identify topics inherent within the clinical narrative component of EHRs. The model comprised 30 topics which were used to annotate EHRs spanning the natural disease outbreak and investigate whether any given topic might mirror the outbreak time-course. Narratives were annotated using the Gensim Library LdaModel module for the topic best representing the text within them. Counts for narratives labelled with one of the topics significantly matched the disease outbreak based on the practitioner-derived 'gastroenteric' MPC (Spearman correlation 0.978); no other topics showed a similar time course. Using artificially injected outbreaks, it was possible to see other topics that would match other MPCs including respiratory disease. The underlying topics were readily evaluated using simple word-cloud representations and using a freely available package (LDAVis) providing rapid insight into the clinical basis of each topic. This work clearly shows that unsupervised record annotation using topic modelling linked to simple text visualisations can provide an easily interrogable method to identify and characterise outbreaks and other anomalies of known and previously un-characterised diseases based on changes in clinical narratives.
| S-EPMC8659617 | biostudies-literature
Project description:BACKGROUND: The Food and Drug Administration (FDA) approved drug labels contain a broad array of information, ranging from adverse drug reactions (ADRs) to drug efficacy, risk-benefit consideration, and more. However, the labeling language used to describe these information is free text often containing ambiguous semantic descriptions, which poses a great challenge in retrieving useful information from the labeling text in a consistent and accurate fashion for comparative analysis across drugs. Consequently, this task has largely relied on the manual reading of the full text by experts, which is time consuming and labor intensive. METHOD: In this study, a novel text mining method with unsupervised learning in nature, called topic modeling, was applied to the drug labeling with a goal of discovering "topics" that group drugs with similar safety concerns and/or therapeutic uses together. A total of 794 FDA-approved drug labels were used in this study. First, the three labeling sections (i.e., Boxed Warning, Warnings and Precautions, Adverse Reactions) of each drug label were processed by the Medical Dictionary for Regulatory Activities (MedDRA) to convert the free text of each label to the standard ADR terms. Next, the topic modeling approach with latent Dirichlet allocation (LDA) was applied to generate 100 topics, each associated with a set of drugs grouped together based on the probability analysis. Lastly, the efficacy of the topic modeling was evaluated based on known information about the therapeutic uses and safety data of drugs. RESULTS: The results demonstrate that drugs grouped by topics are associated with the same safety concerns and/or therapeutic uses with statistical significance (P<0.05). The identified topics have distinct context that can be directly linked to specific adverse events (e.g., liver injury or kidney injury) or therapeutic application (e.g., antiinfectives for systemic use). We were also able to identify potential adverse events that might arise from specific medications via topics. CONCLUSIONS: The successful application of topic modeling on the FDA drug labeling demonstrates its potential utility as a hypothesis generation means to infer hidden relationships of concepts such as, in this study, drug safety and therapeutic use in the study of biomedical documents.
| S-EPMC3236833 | biostudies-other
Project description:In supervised machine learning, specifically in classification tasks, selecting and analyzing the feature vector to achieve better results is one of the most important tasks. Traditional methods such as comparing the features' cosine similarity and exploring the datasets manually to check which feature vector is suitable is relatively time consuming. Many classification tasks failed to achieve better classification results because of poor feature vector selection and sparseness of data. In this paper, we proposed a novel framework, topic2features (T2F), to deal with short and sparse data using the topic distributions of hidden topics gathered from dataset and converting into feature vectors to build supervised classifier. For this we leveraged the unsupervised topic modelling LDA (latent dirichlet allocation) approach to retrieve the topic distributions employed in supervised learning algorithms. We made use of labelled data and topic distributions of hidden topics that were generated from that data. We explored how the representation based on topics affect the classification performance by applying supervised classification algorithms. Additionally, we did careful evaluation on two types of datasets and compared them with baseline approaches without topic distributions and other comparable methods. The results show that our framework performs significantly better in terms of classification performance compared to the baseline(without T2F) approaches and also yields improvement in terms of F1 score compared to other compared approaches.
| S-EPMC8372003 | biostudies-literature
Project description:Detailed literature search and writing is very important for the success of long research projects, publications and theses. Search engines provide significant convenience in research processes. However, conducting a comprehensive and systematic research on the web requires a long working process. In order to make literature searches effective, simple and comprehensive, various libraries and development tools have been created and made available. By using these development tools, research processes that may take days can be reduced to hours or even minutes. Literature review is not only necessary for academic studies, but it is a process that should be used and performed in every field where new approaches are adopted. Literature review is a process that gives us important ideas about whether similar studies have been conducted before, which methods have been used before and what has not been addressed in previous studies. It is also of great importance in terms of preventing possible copyright problems in future studies. The main purpose of this study is to propose an application that will facilitate, speed up and increase the efficiency of literature searches. In existing systems, literature searches are performed by browsing search sites or various article sites one by one and using the search tools provided by these sites. It is simple to use, allows the entire World Wide Web environment to be searched, and provides the user with the search findings. In this study, we have implemented an application that allows the crawling of the entire World Wide Web environment, is very simple to use, and quickly presents the crawl findings to the user.
| S-EPMC11623055 | biostudies-literature