The presentation contains about how to classify the sentiments or sentiment analysis. Especially there are positive or negative emotions. So to classify them we have used python language by taking the help of nltk package.

1. Twitter Sentiment Analysis using
Python and NLTK
Presentation by:
ASHWIN PERTI,
Department of IT

2. Sentiment Analysis using PYTHON
The purpose of this Sentiment Analysis is:
● Able to automatically classify a tweet as a
positive
OR
● Negative tweet Sentiment wise

3. Sentiment Analysis using PYTHON
● The classifier needs to be trained:
● We need a list of manually classified tweets.

4. Positive Tweets
● I love this car
● This view is amazing
● I feel great this morning
● I am so excited about the concert
● He is my best friend

5. Negative Tweets
● I do not like this car
● This view is horrible
● I feel tired this morning
● I am not looking forward to the concert
● He is my enemy

6. Test Tweets
● TEST SET – to assess the exactitude of the
trained classifier
● I feel happy this morning. positive
● Larry is my friend. positive
● I do not like that man. negative
● My house not great. negative
● Your song annoying. negative

7. CLASSIFIER
● The list of word features need to be extracted
from the tweets.
● It is a list with every distinct words ordered by
frequency of appearance.

8. CLASSIFIER – Feature Extractor
● To decide which features are more relevant.
● The one we are going to use returns a
dictionary indicating that words are contained
in the input passed.
● INPUT - tweet

9. classifier=nltk.NaiveBayesClassifier.
train(training_set)

10. Naive Bayes Classifier
● It uses the prior probability of each label – which is
the frequency of each label in the training set and the
contribution from each feature.
● In our case, the frequency of each label is the same
for 'positive' and 'negative'.
● Word 'amazing' appears in 1 of 5 of the positive
tweets and none of the negative tweets.
● This means that the likelihood of the 'positive' label
will be multiplied by 0.2 when this word is seen as
part of the input.

11. CLASSIFY
● Now that we have our classifier initialized,
● Classify a tweet and
● See what the sentiment type output is:
● Our classifier is able to detect that this tweet
has a positive sentiment because
● Of the word 'friend'
● Which is associated to the positive tweet:
● 'He is my best friend'

12. print extract_features(tweet2.split())
● 'contains(looking)': False,
'contains(feel)': False,
● 'contains(the)': False,
● 'contains(excited)': False,
● 'contains(about)': False,
● 'contains(great)': False,
● 'contains(horrible)': False,
● 'contains(car)': False,
● 'contains(this)': False,
● 'contains(best)': False,
● 'contains(friend)': True,
● 'contains(concert)': False,
● 'contains(forward)': False,
● 'contains(view)': False,
● 'contains(tired)': False,
● 'contains(like)': False,
● 'contains(love)': False,
● 'contains(amazing)': False,
● 'contains(enemy)': False,
● 'contains(not)': True,
● 'contains(morning)': False}