corono_tweet_sent

target column: Sentiment

Preprocessing:

1. convert date column to pandas datetime format
2. drop duplicate tweets
3. extract columns OriginalTweet, Sentiment
4. clean the OriginalTweet by removing:

• emojis
• newlines and convert text to lowercase
• links and mentions
• non utf8/ascii characters
• hashtags
• & and $ present in words
• multiple spaces

5. find the length of the cleaned text to see if after cleaning, there are tweets with 0 words. So drop tweets with <5 words
6. tokenize the tweets using BERT tokenizer
7. remove tweets with token lengths >80 as they don't appear to be in English
8. convert Sentiment column to have only 3 columns: Negative: 0, Neutral: 1, Positive: 2

Balance the Dataset

Balanced dataset for a model would generate

• higher accuracy models
• higher balanced accuracy
• balanced detection rate We use randomoversampler:
• Randomly duplicate examples in the minority class.

Validation set extracted from Train set to avoid overfitting

Apply One-Hot Encoding:

We convert each categorical value into a new categorical column and assign a binary value of 1 or 0 to those columns. Each integer value is represented as a binary vector. All the values are zero, and the index is marked with a 1.

Fit data to the Machine Learning models:

1. Naive Bayes Classifier

2. Logistic Regression Classifier

3. Random Forest Classifier

4. K Nearest Neighbours Classifier

Metrics for ML Algorithms:

• Precision : TP/TP+FP
• Recall : TP/TP+FN
• F1 Score : 2 * Precision * Recall / (Precision + Recall)

Results:

1. Naive Bayes Classifier: F1 Score: 70%

2. Logistic Regression Classifier: F1 Score: 78%

3. Random Forest Classifier: F1 Score: 67%

4. K Nearest Neighbours Classifier: F1 Score: 47%

Fit data to the Deep Learning Models:

1. BERT

optimizer: Adam
learning rate: 1^-5
decay rate: 1^-7
epochs: 1
loss: categorical cross entropy
accuracy: categorical accuracy
input layer: 128 neurons
output layer: 3 neurons
activation: softmax

2. RoBERTa

optimizer: Adam
learning rate: 1^-5
decay rate: 1^-7
epochs: 1
loss: categorical cross entropy
accuracy: categorical accuracy
input layer: 128 neurons
output layer: 3 neurons
activation: softmax

Metrics for Deep Learning Algorithms:

• Precision : TP/TP+FP
• Recall : TP/TP+FN
• F1 Score : 2 * Precision * Recall / (Precision + Recall)
• Confusion Matrix

Results:

1. BERT: F1 Score: 85%
2. RoBERTa: F1 Score: 88%