Getting started

The topics-package enables Differential Language Analysis using words, phrases and topics

Please reference our tutorial article when using the package: Language visualisation methods for psychological assessments and 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..

This Getting Started tutorial is going through the most central topics functions.

Usage

In an example where the topics are used to predict the PHQ-9 score, the pipeline can be run as follows:

1. Data Preprocessing
To preprocess the data, run the following command:


library(topics)
#> 
#> This is topics: your text's new best friend (version 0.40.2).
#> Please note that the topics package requires you to download and install java from www.java.com. 
#> 
#> For more information about the topics package see www.r-topics.org and www.r-text.org.

dtm <- topicsDtm(
  data = dep_wor_data$Depword)

# Check the results from the dtm and refine stopwords and removal rates if necessary
dtm_evaluation <- topicsDtmEval(
  dtm)
dtm_evaluation$frequency_plot

2. Model Training
To train the LDA model, run the following command:


model <- topicsModel(
  dtm = dtm,
  num_topics = 20,
  num_iterations = 1000)

3. Model Inference
To infer the topic term distribution of the documents, run the following command:


preds <- topicsPreds(
  model = model,
  data = dep_wor_data$Depword)

4. Statistical Analysis
To analyze the relationship between the topics and the prediction variable, run the following command:


test <- topicsTest(
  data = dep_wor_data,
  model = model,
  preds = preds,
  x_variable = "PHQ9tot",
  controls = c("Age"),
  test_method = "linear_regression")

5. Visualization
To visualize the significant topics as wordclouds, run the following command:


plot_list <- topicsPlot(
  model = model,
  test = test,
  figure_format = "png")

# showing some of the plots
plot_list$square1
#> $t_5

Articles using the topics-package

Differentiating balance and harmony through natural language analysis: A cross-national exploration of two understudied wellbeing-related concepts

Other relevant references

The below list consists of papers analyzing human language in a similar fashion that is possible in topics.

Methods Articles

Gaining insights from social media language: Methodologies and challenges..
Kern et al., (2016). Psychological Methods.

Computer Science: Python Software

DLATK: Differential language analysis toolkit. Schwartz, H. A., Giorgi, et al., (2017). In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

DLATK

- Usage