README.md

In topics: Creating and Significance Testing Language Features for Visualisation

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. 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 transforms voice recordings into text, audio features, or embeddings.
• text 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 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.

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.

Installation

The topics package uses JAVA, which is another programming language. Please start by downloading and installing it from www.java.com/en/download/. Then open R and run:

install.packages("devtools")
devtools::install_github("theharmonylab/topics")

# if you run in to any installation problem, try installing rJava first.

# Before open the library, consider setting this option (can increase 5000);  without it the code may ran out of memory
options(java.parameters = "-Xmx5000m")

Table of Contents

1. Overview
2. Installation
3. Usage

Overview

The pipeline is composed of the following steps:

1. Data Preprocessing The data preprocessing converts the data into a document term matrix (DTM) and removes stopwords, punctuation, etc. which is the data format needed for the LDA model.

2. Model Training The model training step trains the LDA model on the DTM with a number of iterations and predefined amount of topics.

3. Model Inference The model inference step uses the trained LDA model to infer the topic term distribution of the documents.

4. Statistical Analysis The analysis includes the methods like linear regression, binary regression, ridge regression or correlation to analyze the relationship between the topics and the prediction variable. It is possible to control for a number of variables and to adjust the p-value for multiple comparisons.

5. Visualization The visualization step creates wordclouds of the significant topics found by the statistical analysis.

topics documentation built on Aug. 29, 2025, 5:15 p.m.