Merge pull request #253 from The-Strategy-Unit/ChrisBeeley/issue250

Chris beeley/issue250

presentations/2024-11-27_text-mining/index.qmd

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+---
+title: "Text mining"
+subtitle: "What it can and can't do, and what it could do"
+author: 
+  - "[Chris Beeley](mailto:[email protected])"
+date: 2024-11-27
+date-format: "D MMMM YYYY"
+format:
+  revealjs:
+    theme: [default, ../su_presentation.scss]
+    transition: none
+    chalkboard:
+      buttons: false
+    preview-links: auto
+    slide-number: false
+    auto-animate: true
+    footer: |
+      Learn more about [The Strategy Unit](https://www.strategyunitwm.nhs.uk/)
+---
+
+## Intro
+
+* Range of possible applications
+* There are a range of caveats depending on the use case
+* Some of them are more relevant than others
+* Some of them are "free", and some aren't
+
+## What is text mining?
+
+* A variety of supervised and unsupervised methods
+* The uses we'll discuss today have no "understanding" of text (no "intelligence")
+* They all have the potential, therefore, to be very inaccurate (e.g. negation)
+* But they can be a low cost way of gathering insight, properly applied
+
+## "I just want a bit of help sorting"
+
+* This is one of the safer things to do
+* There are a range of methods
+* Some will give you some say of the "theme" of the piles, and some don't
+* Great for: sifting, looking at relative size of piles, novel suggestions for themes for text
+* Bad for: accuracy, control over what's in the piles
+
+## "I just want to find useful examples"
+
+* Also quite a safe application, and one we implemented for patient experience
+* The algorithm is just helping you to find things with a particular theme or sentiment
+* You bring the understanding
+* Many ways of achieving this, from easy to difficult
+  * Unsupervised and supervised
+  * Searching for strings vs semantic search
+
+## Generating summary statistics
+
+* This needs to be done with care
+* You can potentially lose a lot of nuance and meaning
+* Even the best model is probably only around 80% accurate
+* Useful for monitoring and making rough estimates about the size of things
+* Not suitable for anything that needs accuracy (e.g. safeguarding)
+
+## "Free"
+
+* Unsupervised learning is "free"- no labelling necessary
+* Arguably you may as well use it for everything, speculatively
+* However there are lots of models and parameters to set
+* So "free" is not really "free"
+
+## What's going on?
+
+* Text models are only as sensible as their inputs
+* We call the algorithm that turns text to numbers a "vectoriser"
+
+## Bag of words vs TF-IDF
+
+* Bag of words just counts the number of times each word appears
+* Crude but effective
+* TF-IDF works similarly but makes rare words more important, which can help with topic modelling and classification
+
+## Word embeddings 
+
+![](vectors.png)
+
+From [Sutor et al.](https://ieeexplore.ieee.org/document/8695402), reproduced under fair use
+
+## Word embeddings cont.
+
+* There are some smallish ones (like Glove), and some huge ones (like BERT)
+* Vectors are the only way to encode meaning and context
+
+## The future
+
+* A number of different things suggest themselves
+* Use of topic models to explore and search
+* Training of a supervised model for a particular project
+
+## The dream
+
+* Zero shot model with human in the loop learning
+