Sentiment analysis on Dataframe

Question

I have a dataframe 'df' which has data like :

        State                                               Text
0  California  This is a beutiful day# It's too hard I am get...
1     Florida    Can somebody please help me; I am new to python
2    New York  But I am stuck with code How should I solve th...

This dataframe is created from a csv file using below code :

delimiter = ' '
df =  df2.groupby('State')['Text'].apply(lambda x: "%s" % delimiter.join(x)).reset_index()

I need to do sentiment analysis(using TextBlob) on this dataframe 'df' state wise. Could anyone please help me to do the sentiment analysis state wise. I tried to do it as:

for row in df.itertuples():
    text = df.iloc[:, 1].tolist()
    tweets = " ".join(str(x) for x in text)
    text = TextBlob(tweets)
    score = text.sentiment

But it gave me sentiment score of total dataframe, not sentiment score for each state separately

My code gave output as :

Sentiment(polarity=-0.07765151515151517, subjectivity=0.49810606060606055)

But i want sentiment output for each row(that means for each state) separately.

Answer 1

You can use apply() in combination with a lambda function. This is a much more efficient way than looping.

df[['polarity', 'subjectivity']] = df['Text'].apply(lambda Text: pd.Series(TextBlob(Text).sentiment))

This returns:

    State   Text    polarity    subjectivity
0   California  This is a beutiful day# It's too hard I am get  -0.291667   0.541667
1   Florida Can somebody please help me; I am new to python 0.136364    0.454545
2   New York    But I am stuck with code How should I solve th  0.000000    0.000000