flair.py

# import libraries
import pandas as pd
import seaborn as sns
import flair

# configure size of heatmap
sns.set(rc={'figure.figsize':(35,3)})

# function to visualize
def visualise_sentiments(data):
  sns.heatmap(pd.DataFrame(data).set_index("Sentence").T,center=0, annot=True, cmap = "PiYG")

# model
flair_sentiment = flair.models.TextClassifier.load('en-sentiment')

# text
sentence = "To inspire and guide entrepreneurs is where I get my joy of contribution"

# sentiment
s = flair.data.Sentence(sentence)
flair_sentiment.predict(s)
total_sentiment = s.labels
total_sentiment

# tokenize sentiments
tokens = [token.text for token in s.tokens]
ss = [flair.data.Sentence(s) for s in tokens]
[flair_sentiment.predict(s) for s in ss]
sentiments = [s.labels[0].score * (-1,1)[str(s.labels[0]).split()[0].startswith("POS")] for s in ss]

# heatmap
visualise_sentiments({
      "Sentence":["SENTENCE"] + tokens,
      "Sentiment":[total_sentiment[0].score *(-1,1)[str(total_sentiment[0]).split()[0].startswith("POS")]] + sentiments,
})