Splitting TextBlob sentiment analysis results into two separate columns - Python Pandas

Question

from textblob import TextBlob

def sentiment_calc(text):
    try:
        return TextBlob(text).sentiment
    except:
        return None

test_df['sentiment score'] = test_df['text'].apply(sentiment_calc)
test_df

I recently ran a code on my dataset to implement sentiment analysis using the TextBlob package. After running that, my sentiment column has the following output below (I did an example table with dummy numbers below).

 text   | sentiment score
 ------------------------
 nice   | (0.45, 4.33)
 good   | (0.45, 4.33)
 ok     | (0.45, 4.33)

And the output I would like to get is this, where I split the sentiment column into two columns, but add those columns onto the current dataframe.

text | polarity | subjectivity
------------------------------
nice |0.45      | 0.433
good |0.45      | 0.433
ok   |0.45      | 0.433

Is there a way to do this in Python 2.7?

Please edit your question to show the code that produced your example table. — J_H
– J_H, Commented Nov 4, 2017 at 18:14

s3bw · Accepted Answer · 2017-11-04 18:35:42Z

2

This is what you want to do with pandas:

sentiment_series = df['sentiment score'].tolist()

columns = ['polarity', 'subjectivity']

df = pd.DataFrame(sentiment_series, columns=columns, index=df.index)

edited Nov 4, 2017 at 18:35

answered Nov 4, 2017 at 18:29

s3bw

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1 Comment

SRIDHAR STREAKS Over a year ago

This erases the other content on the 'df' data frame since it is replacing values in the data frame. is there any way to concat the new columns to the 'df'

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