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topics

### 📣 Online Workshop: *Analysing Human Language using R*

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# topics

## Overview

An R-package for analyzing natural language implementing Differential
Language Analysis using words, phrases and topics.  
  
Check out our tutorial paper: [Multiple Methods for Visualizing Human
Language: A Tutorial for Social and Behavioural
Scientists](https://osf.io/preprints/psyarxiv/nxfvr_v1/). If you use the
topics package, please cite this tutorial in your work.  
  
The `topics` package is part of the *R Language Analysis Suite*,
including `talk`, `text` and `topics`.

- [`talk`](https://www.r-talk.org/) transforms voice recordings into
  text, audio features, or embeddings.  
    
- [`text`](https://www.r-text.org/) provides many language tasks such as
  converting digital text into word embeddings.  
    
  `talk` and `text` offer access to Large Language Models from Hugging
  Face.  
    
- [`topics`](https://www.r-topics.org/) visualizes language patterns
  into words, phrases or topics to generate psychological insights.  
  The `topics` package supports the `text` package in analysing and
  visualizing topics from BERTtopics.  
    

![](reference/figures/talk_text_topics.svg)

  
When using the `topics` package, please cite:

Ackermann L., Zhuojun G. & Kjell O.N.E. (2024). An R-package for
visualizing text in topics. <https://github.com/theharmonylab/topics>.
`DOI:zenodo.org/records/11165378`.

> ### 📣 Online Workshop: *Analysing Human Language using R*
>
> **August 11–13, 2026** — three half-day sessions (2:00–5:00 pm CEST /
> 8:00–11:00 am ET)
>
> Learn the full *R Language Analysis Suite* —
> [`talk`](https://www.r-talk.org/), [`text`](https://www.r-text.org/),
> and [`topics`](https://www.r-topics.org/) — with Oscar Kjell.
>
> **[Read more & register →](https://smart-workshops.com/lang-r-info)**

## Differential Language Analysis with Topics and N-grams

The `topics` pipeline is designed for a seamless transition from raw
text to statistically grounded visualizations. It is composed of the
following steps:

**1. Data Preprocessing** Transform raw text into a Document-Term Matrix
(DTM) or extract n-grams. This step handles cleaning, including the
removal of stopwords and punctuation, to prepare data for modeling or
frequency analysis.
…

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