Natural Language Processing Algorithm...
0 likes38 views
The document discusses various types of natural language processing (NLP) models, including classical and deep learning approaches, and outlines the steps involved in an NLP project. It includes methods such as bag-of-words, sequence-to-sequence, and the use of naïve Bayes for sentiment analysis in restaurant reviews. Python code examples for predicting the sentiment of reviews are also provided.
Natural Language Processing Algorithm...
- 2. Types of NLP
- 3. Classical Vs Deep Learning Models
- 4. Classical Vs Deep Learning Models 1. If – Else Rules (Chatbot) 2. Audio Frequency Components Analysis (Speech Recognition) 3. Bag-of-Words Model (Classification) 4. CNN for Text Recognition (Classification) 5. Sequence to Sequence (Many Applications)
- 5. Bag of Words
- 7. Bag of Words
- 8. Bag of Words
- 9. Bag of Words
- 10. Bag of Words
- 11. Bag of Words
- 12. Bag of Words
- 13. Bag of Words
- 14. Natural Language Processing-steps 1. Importing the Libraries 2. Importing the Dataset 3. Cleaning the Texts 4. Creating the Bag-of-words Model 5. Splitting the Dataset into Training and Test Sets 6. Training the Naïve Bayes Model on the Training Set 7. Predicting the Test Set Result 8. Making the Confusion Matrix.
- 16. NLP Python Code
- 17. NLP Python Code
- 18. NLP Python Code
- 19. NLP Python Code
- 20. NLP Python Code
- 21. Predicting if a single review is positive or negative Use the model to predict if the following review: "I love this restaurant so much“ is positive or negative. Positive Review Negative Review Use the model to predict if the following review: "I hate this restaurant so much“ is positive or negative.
- 22. Predicting if a single review is positive or negative
- 23. Predicting if a single review is positive or negative