Twitter US Airline Sentiment - Kaggle
Text-Vectorization Techniques used:
- CountVectorizer
- TfidfVectorizer
- OneHotEncoding
ML Algorithms used:
- Logistic Regression
- Naive Bayes
BEST MODEL: TFIDFvectorizer_LogisticRegression
Experiment-2. Multi-Layer Perceptron (MLP) Models with different Model Architectures and Optimizers For Sentiment Analysis
Model Architectures:
- Model-1
Layer (type) - Output Shape
layer_1 (Dense) - (None, 64)
layer_2 (Dense) - (None, 64)
layer_3 (Dense) - (None, 3)
- Model-2
Layer (type) - Output Shape
layer_1 (Dense) - (None, 32)
layer_2 (Dense) - (None, 3)
- Model-3
Layer (type) - Output Shape
layer_1 (Dense) - (None, 10)
layer_2 (Dense) - (None, 3)
Optimizers:
- adam
- rmsprop
- sgd
Model Architectures:
- Simple RNN Model
Layer (type) - Output Shape
embedding_12 (Embedding) (None, 22, 100)
layer_1 (SimpleRNN) (None, 128)
layer_2 (Dense) (None, 10)
output_layer (Dense) (None, 3)
- LSTM Model
Layer (type) - Output Shape
embedding_12 (Embedding) (None, 22, 100)
layer_1 (LSTM) (None, 128)
output_layer (Dense) (None, 3)
- GRU Model
Layer (type) - Output Shape
embedding_12 (Embedding) (None, 22, 100)
layer_1 (GRU) (None, 128)
output_layer (Dense) (None, 3)
- Bidirectional LSTM Model
Layer (type) - Output Shape
embedding_12 (Embedding) (None, 22, 100)
bidirectional_6 (Bidirectional) (None, 128)
output_layer (Dense) (None, 3)