Sentiment analysis in TensorFlow - ML Experiment: Train & Evaluate

Experiment - Sentiment analysis

Problem:We want to classify movie reviews as positive or negative using a neural network. The current model trains well but performs poorly on new data.

Current Metrics:Training accuracy: 95%, Validation accuracy: 70%, Training loss: 0.15, Validation loss: 0.60

Issue:The model is overfitting: it learns the training data too well but does not generalize to validation data.

Your Task

Reduce overfitting so that validation accuracy improves to at least 85% while keeping training accuracy below 92%.

You can only modify the model architecture and training parameters.

Do not change the dataset or preprocessing steps.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

TensorFlow

import tensorflow as tf
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout
from tensorflow.keras.callbacks import EarlyStopping

# Load data
max_features = 10000
maxlen = 200
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=max_features)

# Pad sequences
X_train = pad_sequences(X_train, maxlen=maxlen)
X_test = pad_sequences(X_test, maxlen=maxlen)

# Build model
model = Sequential([
    Embedding(max_features, 128, input_length=maxlen),
    LSTM(64, return_sequences=False),
    Dropout(0.5),
    Dense(32, activation='relu'),
    Dropout(0.5),
    Dense(1, activation='sigmoid')
])

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
              loss='binary_crossentropy',
              metrics=['accuracy'])

# Early stopping
early_stop = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)

# Train model
history = model.fit(X_train, y_train,
                    epochs=20,
                    batch_size=64,
                    validation_split=0.2,
                    callbacks=[early_stop],
                    verbose=2)

# Evaluate on test data
loss, accuracy = model.evaluate(X_test, y_test, verbose=0)

print(f'Test loss: {loss:.4f}, Test accuracy: {accuracy:.4f}')

Added Dropout layers after LSTM and Dense layers to reduce overfitting.

Reduced LSTM units from a higher number to 64 to lower model complexity.

Added EarlyStopping callback to stop training when validation loss stops improving.

Set learning rate to 0.001 for stable training.

Results Interpretation

Before: Training accuracy 95%, Validation accuracy 70%, Training loss 0.15, Validation loss 0.60

After: Training accuracy 90%, Validation accuracy 87%, Training loss 0.30, Validation loss 0.40

Adding dropout and early stopping reduces overfitting, improving validation accuracy while slightly lowering training accuracy. This helps the model generalize better to new data.

Bonus Experiment

Try using a bidirectional LSTM layer instead of a single LSTM layer to see if it improves validation accuracy further.

💡 Hint

Replace the LSTM layer with tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)) and keep other changes the same.

Practice

1. What is the main goal of sentiment analysis in TensorFlow?

easy

A. To translate text into another language

B. To classify text as positive or negative

C. To generate new text automatically

D. To count the number of words in a sentence

Solution

1. Step 1: Understand sentiment analysis purpose

   Sentiment analysis is used to find the feeling or opinion in text, usually positive or negative.

2. Step 2: Match goal with options

   Among the options, only classifying text as positive or negative matches sentiment analysis.

3. Final Answer:

   To classify text as positive or negative -> Option B

4. Quick Check:

   Sentiment analysis = classify positive/negative [OK]

Hint: Sentiment analysis = positive or negative text classification [OK]

Common Mistakes:

• Confusing sentiment analysis with translation
• Thinking it generates new text
• Believing it only counts words

2. Which TensorFlow layer is commonly used to convert words into numbers for sentiment analysis?

easy

A. Dense layer

B. Flatten layer

C. Dropout layer

D. Embedding layer

Solution

1. Step 1: Identify layer for word representation

   Embedding layers convert words into numeric vectors that models can understand.

2. Step 2: Compare with other layers

   Dense layers are for fully connected neurons, Dropout prevents overfitting, Flatten reshapes data, none convert words to numbers.

3. Final Answer:

   Embedding layer -> Option D

4. Quick Check:

   Embedding layer = word to number conversion [OK]

Hint: Embedding layer turns words into numbers for models [OK]

Common Mistakes:

• Choosing Dense layer for word conversion
• Confusing Dropout with embedding
• Using Flatten layer incorrectly

3. What will be the output shape of the embedding layer if input shape is (None, 10) and embedding dimension is 16?

medium

A. (None, 10, 16)

B. (None, 16, 10)

C. (10, 16)

D. (None, 10)

Solution

1. Step 1: Understand embedding input and output shapes

   Input shape (None, 10) means batch size unknown, sequence length 10. Embedding outputs a vector per word with size 16.

2. Step 2: Calculate output shape

   Output shape is (batch_size, sequence_length, embedding_dim) = (None, 10, 16).

3. Final Answer:

   (None, 10, 16) -> Option A

4. Quick Check:

   Embedding output shape = (batch, seq_len, embed_dim) [OK]

Hint: Embedding output shape = input length x embedding size [OK]

Common Mistakes:

• Swapping sequence length and embedding dimension
• Ignoring batch size dimension
• Confusing input and output shapes

4. Identify the error in this TensorFlow model snippet for sentiment analysis:

model = tf.keras.Sequential([
  tf.keras.layers.Embedding(input_dim=1000, output_dim=16, input_length=10),
  tf.keras.layers.Dense(1, activation='sigmoid')
])

medium

A. Dense layer activation should be 'relu' for binary output

B. Embedding layer input_dim should be output_dim

C. Missing Flatten or GlobalPooling layer before Dense

D. input_length parameter is not allowed in Embedding layer

Solution

1. Step 1: Analyze model layer sequence

   Embedding outputs 3D tensor (batch, seq_len, embed_dim). Dense expects 2D input, so flattening or pooling is needed.

2. Step 2: Identify missing layer

   Without Flatten or GlobalPooling, Dense layer will raise shape error.

3. Final Answer:

   Missing Flatten or GlobalPooling layer before Dense -> Option C

4. Quick Check:

   Embedding output must be flattened before Dense [OK]

Hint: Add Flatten or pooling before Dense after Embedding [OK]

Common Mistakes:

• Confusing input_dim and output_dim in Embedding
• Using wrong activation for binary classification
• Thinking input_length is invalid parameter

5. You want to improve a sentiment analysis model's accuracy on short reviews. Which approach is best?

hard

A. Increase embedding dimension and add a Bidirectional LSTM layer

B. Reduce embedding dimension and remove all recurrent layers

C. Use only a Dense layer without embedding

D. Train on fewer examples to avoid overfitting

Solution

1. Step 1: Consider model improvements for short text

   Increasing embedding dimension helps capture more word features; Bidirectional LSTM captures context from both directions.

2. Step 2: Evaluate other options

   Reducing embedding or removing recurrent layers loses information; training on fewer examples reduces learning quality.

3. Final Answer:

   Increase embedding dimension and add a Bidirectional LSTM layer -> Option A

4. Quick Check:

   More embedding + Bidirectional LSTM = better short text accuracy [OK]

Hint: Use bigger embeddings and bidirectional LSTM for short texts [OK]

Common Mistakes:

• Thinking smaller embeddings improve accuracy
• Removing recurrent layers harms context understanding
• Training on fewer examples reduces model learning