Package: SportMiner 0.1.0

SportMiner: Text Mining and Topic Modeling for Sport Science Literature

A comprehensive toolkit for mining, analyzing, and visualizing scientific literature in sport science domains. Provides functions for retrieving abstracts from 'Scopus', preprocessing text data, performing advanced topic modeling using Latent Dirichlet Allocation ('LDA'), Structural Topic Models ('STM'), and Correlated Topic Models ('CTM'), and creating publication-ready visualizations including keyword co-occurrence networks and topic trends. For methodological details see Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993> for 'LDA', Roberts et al. (2014) <doi:10.1111/ajps.12103> for 'STM', and Blei and Lafferty (2007) <doi:10.1214/07-AOAS114> for 'CTM'.

Authors:Praveen D Chougale [aut, cre], Usha Ananthakumar [aut]

SportMiner_0.1.0.tar.gz
SportMiner_0.1.0.zip(r-4.7-any)SportMiner_0.1.0.zip(r-4.6-any)SportMiner_0.1.0.zip(r-4.5-any)
SportMiner_0.1.0.tgz(r-4.6-any)SportMiner_0.1.0.tgz(r-4.5-any)
SportMiner_0.1.0.tar.gz(r-4.7-any)SportMiner_0.1.0.tar.gz(r-4.6-any)
SportMiner_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SportMiner/json (API)

# Install 'SportMiner' in R:
install.packages('SportMiner', repos = c('https://praveenmaths89.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/praveenmaths89/sportminer/issues

On CRAN:

Conda:

biomechanicsphdsports-science

4.48 score 214 downloads 13 exports 102 dependencies

Last updated from:3ad9137197. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK188
source / vignettesOK370
linux-releaseOK319
macos-releaseOK151
macos-oldrelOK128
windows-develOK152
windows-releaseOK386
windows-oldrelOK132
wasm-releaseOK249

Exports:sm_compare_modelssm_create_dtmsm_get_indexed_keywordssm_keyword_networksm_plot_topic_frequencysm_plot_topic_termssm_plot_topic_trendssm_preprocess_textsm_search_scopussm_select_optimal_ksm_set_api_keysm_train_ldatheme_sportminer

Dependencies:askpassbackportsbase64encBHbroomcachemclicodetoolscpp11curldata.tabledigestdplyrfarverfastmapfastmatchfloatforeachgenericsggforceggplot2ggraphggrepelglmnetgluegraphlayoutsgridExtragtablegtoolshttrigraphisobandISOcodesiteratorsjaneaustenrjsonlitelabelinglatticeldalgrlifecyclemagrittrMASSMatrixMatrixExtramatrixStatsmemoisemimemlapimodeltoolsNLPopensslpillarpkgconfigplyrpolyclippurrrquadprogquantedaR6RColorBrewerRcppRcppArmadilloRcppEigenRcppProgressreshape2RhpcBLASctlrlangrscopusrsparseRSpectraS7scalesshapeslamSnowballCstmstopwordsstringistringrsurvivalsyssystemfontstext2vectextmineRtibbletidygraphtidyrtidyselecttidytexttmtokenizerstopicmodelstweenrutf8vctrsviridisviridisLitewidyrwithrxml2yaml

Getting Started with SportMiner
Introduction | Installation | Setting Up Your Scopus API Key | Step 1: Retrieve Papers from Scopus | Step 2: Preprocess Text Data | Step 3: Create Document-Term Matrix | Step 4: Select Optimal Number of Topics | Step 5: Train Topic Model | Step 6: Visualize Topics | Top Terms per Topic | Topic Frequency Distribution | Topic Trends Over Time | Step 7: Keyword Co-occurrence Network | Advanced: Compare Multiple Models | Customizing Visualizations | Best Practices | Next Steps | Citation | References
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-01-12
Started: 2026-01-12

SportMiner: Text Mining and Topic Modeling for Sport Science Literature
Introduction | Motivation | Related Work | Contributions | Installation and Setup | Installation | API Configuration | Complete Workflow Example | Step 1: Literature Retrieval | Basic Query Syntax | Boolean Operators and Filters | Available Search Filters | Execute Search | Step 2: Text Preprocessing | Step 3: Document-Term Matrix | Step 4: Optimal Topic Number Selection | Step 5: Train Topic Model | Step 6: Model Comparison | Step 7: Visualization | Topic Terms Visualization | Topic Frequency Distribution | Topic Trends Over Time | Keyword Co-occurrence Network | Advanced Usage | Custom Preprocessing | Hyperparameter Tuning | Exporting Results | Case Study: Sports Analytics Literature | Research Question | Method | Results Interpretation | Computational Performance | Benchmarks | Optimization Tips | Best Practices | Reproducibility | Query Design | Preprocessing Decisions | Model Selection | Visualization | Summary | Key Features | Future Development | Acknowledgments | References | Computational Details | Appendix: Function Reference | Data Retrieval Functions | Preprocessing Functions | Topic Modeling Functions | Visualization Functions
Rendered fromSportMiner-JSS.Rmdusingknitr::rmarkdown

Last update: 2026-01-12
Started: 2026-01-12