Tagging sentiment

This tutorials shows you how to do sentiment analysis in Flair.

Tagging sentiment with our standard model​

Our standard sentiment analysis model uses distilBERT embeddings and was trained over a mix of corpora, notably the Amazon review corpus, and can thus handle a variety of domains and language.

Let’s use an example sentence:

from flair.nn import Classifier
from flair.data import Sentence

# load the model
tagger = Classifier.load('sentiment')

# make a sentence
sentence = Sentence('This movie is not at all bad.')

# predict NER tags
tagger.predict(sentence)

# print sentence with predicted tags
print(sentence)

This should print:

Sentence[8]: "This movie is not at all bad." → POSITIVE (0.9929)

Showing us that the sentence overall is tagged to be of POSITIVE sentiment.

Tagging sentiment with our fast model

We also offer an RNN-based variant which is faster but less accurate. Use it like this:

from flair.nn import Classifier
from flair.data import Sentence

# load the model
tagger = Classifier.load('sentiment-fast')

# make a sentence
sentence = Sentence('This movie is very bad.')

# predict NER tags
tagger.predict(sentence)

# print sentence with predicted tags
print(sentence)

This should print:

Sentence[6]: "This movie is very bad." → NEGATIVE (0.9999)

This indicates that the sentence is of NEGATIVE sentiment. As you can see, its the same code as above, just loading the ‘sentiment-fast’ model instead of ‘sentiment’.

List of Sentiment Models

We end this section with a list of all models we currently ship with Flair:

ID	Language	Task	Training Dataset	Accuracy
‘sentiment’	English	detecting positive and negative sentiment (transformer-based)	movie and product reviews	98.87
‘sentiment-fast’	English	detecting positive and negative sentiment (RNN-based)	movie and product reviews	96.83
‘de-offensive-language’	German	detecting offensive language	GermEval 2018 Task 1	75.71 (Macro F1)