SportMiner0.1.0 package

Text Mining and Topic Modeling for Sport Science Literature

calculate_exclusivity

Calculate Topic Exclusivity

convert_dtm_to_stm

Convert DTM to STM Format

reorder_within

Helper: Reorder Terms Within Facets

scale_y_reordered

Helper: Scale for Reordered Terms

sm_compare_models

Compare Multiple Topic Models

sm_create_dtm

Create Document-Term Matrix

sm_get_indexed_keywords

Get Indexed Keywords from Scopus

sm_keyword_network

Create Keyword Co-occurrence Network

sm_plot_topic_frequency

Plot Topic Frequency Distribution

sm_plot_topic_terms

Plot Topic Term Probabilities

sm_plot_topic_trends

Plot Topic Trends Over Time

sm_preprocess_text

Preprocess Text for Topic Modeling

sm_search_scopus

Search Scopus Database

sm_select_optimal_k

Select Optimal Number of Topics

sm_set_api_key

Set Scopus API Key

sm_train_lda

Train LDA Topic Model

SportMiner-package

SportMiner: Text Mining and Topic Modeling for Sport Science Literatur...

theme_sportminer

SportMiner Custom ggplot2 Theme

Download source packageRead PDF manual

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'.

  • Maintainer: Praveen D Chougale
  • License: MIT + file LICENSE
  • Last published: 2026-01-17

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