You can easily analyze the positivity or negativity of the texts by VADER Sentiment. Analysis
You can install the vanderSentiment by the following code:
pip install vaderSentiment
VADER (Valence Aware Dictionary and sEntiment Reasoner) is a rule-based sentiment analysis tool that is specifically attuned for the sentiments that are being expressed in social media. VADER uses a combination of A sentiment lexicon is a list of lexical features (e.g., words) which are generally labeled according to their semantic orientation as either positive or negative. VADER not only tells about the Positivity and Negativity score but also tells us about how positive or negative sentiment is.
You can refer the following code:
# import SentimentIntensityAnalyzer class
# from vaderSentiment.vaderSentiment module. from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
# function to print sentiments
# of the sentence. def sentiment_scores(sentence):
# Create a SentimentIntensityAnalyzer object. sid_obj = SentimentIntensityAnalyzer()
# polarity_scores method of SentimentIntensityAnalyzer
# oject gives a sentiment dictionary.
# which contains pos, neg, neu, and compound scores. sentiment_dict = sid_obj.polarity_scores(sentence) print("Overall sentiment dictionary is : ", sentiment_dict)
print("sentence was rated as ", sentiment_dict['neg']*100, "% Negative")
print("sentence was rated as ", sentiment_dict['neu']*100, "% Neutral")
print("sentence was rated as ", sentiment_dict['pos']*100, "% Positive")
print("Sentence Overall Rated As", end = " ")
# decide sentiment as positive, negative and neutral
if sentiment_dict['compound'] >= 0.05 : print("Positive") elif sentiment_dict['compound'] <= - 0.05 : print("Negative") else :
print("Neutral")
# Driver code if __name__ == "__main__" :
print("\n1st statement :") sentence = "Geeks For Geeks is the best portal for \ the computer science engineering students."
# function calling sentiment_scores(sentence)
print("\n2nd Statement :") sentence = "study is going on as usual" sentiment_scores(sentence)
print("\n3rd Statement :") sentence = "I am vey sad today." sentiment_scores(sentence)
The output score is a metric that calculates the sum of all the ratings which have been normalized between -1(most extreme negative) and +1 (most extreme positive).
positive sentiment : (compound score >= 0.05)
neutral sentiment : (compound score > -0.05) and (compound score < 0.05)
negative sentiment : (compound score <= -0.05)
Hope this helps!!!
If you wish to know NLP and Machine Learning for Sentiment Analysis then visit this Machine Learning Course.
