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A Flask LIME explainer app for fine-grained sentiment classification.

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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.

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A Flask LIME explainer app for fine-grained sentiment classification.

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