You can do this lab on your own Unix machine, in IPython Notebook on Google Colab or on Kaggle.
For this lab we will use the fastText library from FAIR for training word2vec models and a classifier.
We will use a dataset of 4M Amazon reviews labelled by sentiment in the fastText format.
1- and 2-star reviews we consider to have negative sentiment, 4- and 5-star reviews positive sentiment.
We just throw away 3-star reviews.
The dataset is already split into train and test files in the fastText format.
__label__<X> __label__<Y> ... <Text>
For example:
__label__positive The book was really exciting, top read for the year!
In this case, the classes are __label__1 and __label__2, and there is only one class per row.
__label__1 corresponds to 1- and 2-star reviews, and __label__2 corresponds to 4- and 5-star reviews.
wget https://storage.googleapis.com/amazonreviews/train.ft.txt.bz2
wget https://storage.googleapis.com/amazonreviews/test.ft.txt.bz2
bzip2 -d train.ft.txt.bz2
bzip2 -d test.ft.txt.bz2
Now do head or tail to see a few rows.
./fasttext supervised -input train.ft.txt -output model_amzn
./fasttext test model_amzn.bin test.ft.txt