An Explainer App for Fine Grained Sentiment Classification
This repo contains an initial prototype of an interactive application written in Flask, that explains the results of fine-grained sentiment classification, described in detail in this Medium Series.
A number of classifiers are implemented and their results explained using the LIME explainer.
The classifers were trained on the Stanford Sentiment Treebank (SST-5) dataset. The class labels are any of [1, 2, 3, 4, 5], where 1 is very negative and 5 is very positive.
Installation
First, set up virtual environment and install from requirements.txt:
python3 -m venv venv
source venv/bin/activate
pip3 install -r requirements.txt
For further development, simply activate the existing virtual environment.
source venv/bin/activate
Usage
Run the file app.py and then enter a sentence, choose a type of classifier and click on the button Explain results!. We can then observe the features (i.e. words or tokens) that contributed to the classifier predicting a particular class label.
Demo for the front-end
The front-end app takes in a text sample and outputs LIME explanations for the different methods. The app is is deployed using Heroku at this location: https://sst5-explainer.herokuapp.com/
Play with your own text examples as shown below and see the fine-grained sentiment results explained!
NOTE: Because the PyTorch-based models (Flair and the causal transformer) are quite expensive to run inference with (they require a GPU), these methods are not deployed.
