Using VADER for sentiment analysis …

Let’s dive into an example, below example is from one my project in which I used VADER for analyzing notes from a Mass Mobilization Project that provides data for protests from all around the world.

Step 1: Create corpus from text column

corpus=list(df[‘notes’])

Step 2 : Insatiate Sentiment Analyzer

#instantiate SIA
sia=SentimentIntensityAnalyzer()#Get polarity scores for the first titlesia.polarity_scores(corpus[0])

Step 3: Create a data frame using each document in your corpus

for text in corpus:scores = sia.polarity_scores(text)scores['text'] = textdicts.append(scores)df_new = pd.DataFrame(dicts)df_new.head()

And you get below data frame :

Lets sort data frame by positive or negative scores and see the results:

df_new.sort_values(by='pos', ascending=False).head(10)

df_new.sort_values(by='neg', ascending=False).head(10)

Protesterviolence and Stateresponse are two target variables used for EDA.

Below is average positive , negative and compound score against protester violence.

df_new.groupby('protesterviolence').mean()[['pos', 'neg', 'compound']].plot(kind='barh',title='Average Positive, Negative & Compound Scores for Protester Violence',figsize=(15,10),color=(['blue','orange','red']))

df_new.groupby('stateresponse').mean()[['pos', 'neg', 'compound']].plot(kind='barh',title='Average Positive, Negative & Compound Scores for State response',figsize=(15,10),color=(['blue','orange','red']))