Sentiment analysis on a csv file using textblob

Question

I used sentiment analysis on a CSV file and the output prints the polarity and subjectivity of a sentence. How can I get the output in a table format along with the classification of the sentence as positive or negative or neutral added to it?

    import csv
    from textblob import TextBlob

    infile = 'sentence.csv'

    with open(infile, 'r') as csvfile:
        rows = csv.reader(csvfile)
    for row in rows:
        sentence = row[0]
        blob = TextBlob(sentence)
        print (sentence)
        print (blob.sentiment.polarity, blob.sentiment.subjectivity)

the output for my code is :

    i am very happy
    1.0 1.0
    its very sad
    -0.65 1.0
    they are bad
    -0.6999999999999998 0.6666666666666666
    hate the life
    -0.8 0.9
    she is so fantastic
    0.4 0.9

Thanks in advance.

Answer 1

I would recommend creating a list of lists and importing that into a pandas dataframe to get a table structure

import csv
from textblob import TextBlob
import pandas as pd
import numpy as np

infile = 'sentence.csv'
bloblist = list()

with open(infile, 'r') as csvfile:
    rows = csv.reader(csvfile)

for row in rows:
    sentence = row[0]
    blob = TextBlob(sentence)
    bloblist.append((sentence,blob.sentiment.polarity, blob.sentiment.subjectivity))

This will give you a list of lists called bloblist Convert it into a pandas dataframe like

df = pd.DataFrame(bloblist, columns = ['sentence','sentiment','polarity'])

After adding that you can create custom calculations like this:

df['positive'] = np.where(df.sentiment > .5,1,0)