Dear All,

Find the steps in Twitter Sentimental Analysis using Python

1. Import necessary packages

from nltk import NaiveBayesClassifier as nbc
from nltk.tokenize import word_tokenize
from itertools import chain
import csv


2. Read the input file using csv reader and generate a list of those tweets

3. Generate a vocabulary

vocabulary = set(chain(*[word_tokenize(i[0].lower()) for i in training_data]))


4. Generate training data

feature_set = [({i:(i in word_tokenize(sentence.lower())) for i in vocabulary},tag) for sentence, tag in training_data]

5. Train the classifier

classifier = nbc.train(feature_set)

6. Generate output csv file

writer = csv.writer(csvoutput, lineterminator='\n')

7. Generate Test Input

featurized_test_sentence =  {i:(i in word_tokenize(test_sentence.lower())) for i in vocabulary}

8. Classfiy and create output data

row.append(classifier.classify(featurized_test_sentence))
all.append(row)

9. Flush output data to an output csv file

writer.writerows(all)