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Python | Sentiment Analysis using VADER
  • Difficulty Level : Medium
  • Last Updated : 23 Jan, 2019

Sentiment Analysis is the process of ‘computationally’ determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mining, deriving the opinion or attitude of a speaker.

Why sentiment analysis?

  • Business: In marketing field companies use it to develop their strategies, to understand customers’ feelings towards products or brand, how people respond to their campaigns or product launches and why consumers don’t buy some products.
  • Politics: In the political field, it is used to keep track of political view, to detect consistency and inconsistency between statements and actions at the government level. It can be used to predict election results as well! .
  • Public Actions: Sentiment analysis also is used to monitor and analyse social phenomena, for the spotting of potentially dangerous situations and determining the general mood of the blogosphere.

Command to install vaderSentiment :

pip install vaderSentiment

VADER Sentiment Analysis :

VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments 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 a sentiment is.

Below is the 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 :
    elif sentiment_dict['compound'] <= - 0.05 :
    else :
# Driver code
if __name__ == "__main__" :
    print("\n1st statement :")
    sentence = "Geeks For Geeks is the best portal for \
                the computer science engineering students."
    # function calling
    print("\n2nd Statement :")
    sentence = "study is going on as usual"
    print("\n3rd Statement :")
    sentence = "I am vey sad today."

Output :

The Compound score is a metric that calculates the sum of all the lexicon 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)

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