# 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)

    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

### 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