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
# codeprint("GFG")pip install afinn /#installing in windowspip3 install afinn /#installing in linux!pip install afinn#installing in jupyter |
Code: Python code for sentiment analysis using Affin
python3
#importing necessary librariesfrom afinn import Afinnimport pandas as pd#instantiate afinnafn = Afinn()#creating list sentencesnews_df = ['les gens pensent aux chiens','i hate flowers', 'he is kind and smart','we are kind to good people'] # compute scores (polarity) and labelsscores = [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 creationdf = pd.DataFrame()df['topic'] = news_dfdf['scores'] = scoresdf['sentiments'] = sentimentprint(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.
