Python – Sentiment Analysis using Affin

Afinn is the simplest yet popular lexicons used for sentiment analysis developed by Finn Årup Nielsen. It contains 3300+ words with a polarity score associated with each word. In python, there is an in-built function for this lexicon.

Let’s see its syntax-

Installing the library:

python3

# code
print("GFG")
pip install afinn /
#installing in windows
pip3 install afinn /
#installing in linux
!pip install afinn
#installing in jupyter

Code: Python code for sentiment analysis using Affin

python3

#importing necessary libraries
from afinn import Afinn
import pandas as pd

#instantiate afinn
afn = Afinn()

#creating list sentences
news_df = ['les gens pensent aux chiens','i hate flowers',
         'he is kind and smart','we are kind to good people']

# compute scores (polarity) and labels
scores = [afn.score(article) for article in news_df]
sentiment = ['positive' if score > 0
                          else 'negative' if score < 0
                              else 'neutral'
                                  for score in scores]

# dataframe creation
df = pd.DataFrame()
df['topic'] =  news_df
df['scores'] = scores
df['sentiments'] = sentiment
print(df)

Output:

topic  scores sentiments
0  les gens pensent aux chiens     0.0    neutral
1               i hate flowers    -3.0   negative
2           he is kind and smart     3.0   positive
3   we are kind to good people     5.0   positive

The best part of this library package is that one can find score sentiment of different languages as well.

python3

afn = Afinn(language = 'da')

#assigning 'da' danish to the object variable.
afn.score('du er den mest modbydelige tæve')

Output:

-5.0

Thus, Afinn can we used easily to get scores immediately.