topics
Overview
An R-package for analyzing natural language implementing Differential
Language Analysis using words, phrases and topics. 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
andtext
offer access to Large Language Models from Hugging Face.topics
visualizes language patterns into words, phrases or topics to generate psychological insights. Thetopics
package supports thetext
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
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.