Sentiments Analysis using Python and nltk
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The document outlines a presentation on sentiment analysis of tweets using Python and NLTK, focusing on classifying tweets as positive or negative. It discusses the necessity of training a classifier with manually classified tweets, the extraction of features from the tweets, and utilizes a Naive Bayes classifier to determine the sentiment based on the frequency of words. An example is provided to demonstrate how the classifier identifies the sentiment of a tweet by analyzing the presence of certain words.
Sentiments Analysis using Python and nltk
- 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
- 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}