- A small app which uses a pre-calculated model to classify tweets.
- The notebook
tweet-sentiment.ipynb
shows how I trained the model. - The model
tweet-sentiment.pkl
classifies text into a category:negative (0)
,neutral (2)
,positive (4)
. - The purpose of this project was to gain some experience using fast.ai - text classification.
- I am using the sentiment140 data set.
- For more information see fast.ai lessons: 4 here, this post also helped me to create the classifier.
// build and run app
./gradlew build run
// stop app
./gradlew stop
Add your CONSUMER_KEY
, CONSUMER_SECRET
, ACCESS_TOKEN
and ACCESS_TOKEN_SECRET
into twitter.env
.
You will need access to a GPU to run the jupyter notebook.
Fast.ai recommend using a p2.xlarge
instance.
Follow the installation guide here.
localhost:8000