1. ALGORITHM
RECURRENT NEURAL NETWORK
1. Start
2. Load Twitter dataset
3. Do data pre processing
4. Split dataset into training and testing sets
5. Define RNN model
i. Create input layer
ii. Add embedding
iii. Add RNN layers
iv. Add dense layers
v. Add output layer
6. Do Compilation
7. Train model
8. Evaluate model
9. Stop
CONVOLUTIONAL NEURAL NETWORK
1. Start
2. Load Twitter dataset
3. Do data pre processing
4. Split dataset into training and testing sets
5. Define CNN model
i. Create input layer
ii. Apply convolutional layers
iii. Add pooling layers
iv. Include output layer
2. 6. Do compilation
7. Train model
8. Evaluate model
9. Stop
LONG SHORT-TERM NETWORK
1. Start
2. Load Twitter dataset
3. Do data pre processing
4. Split dataset into training and testing sets
5. Define LSTM model
Create input layer
Add embedding layer
Add LSTM layers
Add dense layers
Add output layer
6. Do Compilation
7. Train model
8. Evaluate model
9. Stop
BIDERECTIONAL LONG SHORT-TERM MEMORY
Step 1: Start
Step 2: Read the Comments.
Step 3: Store the Comments as data.
Step 4: Remove the noisy data by data filtering.
Step 5: Create a Bi-LSTM.
3. a. Import training data.
b. Tweet Pre-processing
i. Stop word Removal
ii. Case folding
iii. Mapping unique values
iv. Special Character removal
v. Acronym normalization
vi. Spelling Correction.
c. Train the Bi-LSTM with pre-processed data and BERT Model.
Step 6. Test the Bi-LSTM using New Data.
Step 7. End
