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Example 1: Topics in the City of Austin Community Survey

For this tutorial, we will use the City of Austin's Community Survey. We will pick one open-ended question and extract the main themes from the answers.

Necessary imports

using Downloads, CSV, DataFrames
using Plots
using LLMTextAnalysis
import PlotlyJS, PlotlyDocumenter ## Only for the documentation, not needed for users!
plotlyjs(); # recommended backend for interactivity, install with `using Pkg; Pkg.add("PlotlyJS")`

Prepare the data

Download the survey data

Downloads.download("https://data.austintexas.gov/api/views/s2py-ceb7/rows.csv?accessType=DOWNLOAD",
    joinpath(@__DIR__, "cityofaustin.csv"));

Read the survey data into a DataFrame

df = CSV.read(joinpath(@__DIR__, "cityofaustin.csv"), DataFrame);

Let's select one of the open-ended questions, eg,

col = "Q25 - If there was one thing you could share with the Mayor regarding the City of Austin (any comment, suggestion, etc.), what would it be?"
docs = df[!, col] |> skipmissing |> collect;

Topic Analysis

Index the documents (ie, embed them)

index = build_index(docs)
DocIndex(Documents: 2933, PlotData: None, Topic Levels: None)

Plot the index

  • You use any keywords that you're used to from Plots.jl, eg, to customize the title or size
  • labeler_kwargs allows us control the LLM labeling of topics, I like the latest GPT-3.5-Turbo-1106 for the labelling. We can use any kwargs from PromptingTools.jl
  • You can specify the number of topics to show with k, or the height of the dendrogram to cut at with h (see ?Clustering.hclust)
  • See the detail with ?plot
pl = plot(index;
    title = "City of Austin Community Survey Themes",
    labeler_kwargs = (; model = "gpt3t",))

Voila! We have an interactive explorer of the main themes in the survey in less than 2 minutes and for a few cents!

If you do not want to create any plots, simply call build_clusters!(index; k) and explore the generated topics in index.topic_levels[k] where k is the number of topics.

Tip 1: Zoom in/out on the Information

One of the biggest superpowers of LLMs, is that you can zoom in/out in the abstraction level to help you digest information more gradually. For example, we can start by looking at the top-level themes with k=4:

pl4 = plot(index; k = 4, labeler_kwargs = (; model = "gpt3t",))

Now, we have both the top-level themes and the sub-themes available in index.topic_levels, so we can easily switch between them.

Tip 2: Serialize your Index

We don't want to recompute the index and topics every time we want to explore it, so we can serialize it to disk and load it back later.

using Serialization
serialize("austin-index.jls", index)
index = deserialize("austin-index.jls")

Tip 3: Take Advantage of the Interactivity in PlotlyJS

Remember that with PlotlyJS backend, you can zoom in/out, pan, and hover over the points to see the document text. Also, by single-clicking / double-clicking on the topics in the legend, you can hide/show the topics.

Note: You can save the plot as an HTML file and share it with others while keeping the interactivity.

This page was generated using Literate.jl.