Sentiment analysis in TensorFlow - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is sentiment analysis?

Sentiment analysis is a way to find out if a piece of text shows positive, negative, or neutral feelings.

beginner

What kind of data is used for training a sentiment analysis model?

Text data labeled with emotions or opinions, like movie reviews marked as positive or negative.

intermediate

Why do we use word embeddings in sentiment analysis?

Word embeddings turn words into numbers that show their meaning, helping the model understand text better.

intermediate

What is the role of an LSTM layer in a sentiment analysis model?

LSTM helps the model remember important words and their order in a sentence to understand the sentiment.

beginner

How do we measure if a sentiment analysis model is good?

We check accuracy, which tells us how many texts the model guessed right about their feelings.

What does sentiment analysis predict?

A The feeling behind a text

B The length of a text

C The language of a text

D The author of a text

Sentiment analysis predicts if the text is positive, negative, or neutral.

Which layer helps remember word order in a sentence?

A Dense layer

B Dropout layer

C Embedding layer

D LSTM layer

LSTM layers are designed to remember sequences, like word order.

What is a common metric to check sentiment model performance?

A Accuracy

B Batch size

C Epoch

D Loss

Accuracy shows how many predictions the model got right.

Why use word embeddings in sentiment analysis?

A To remove stop words

B To convert words into numbers that capture meaning

C To count the number of words

D To translate text

Word embeddings turn words into meaningful number vectors.

What type of data is needed to train a sentiment analysis model?

A Audio files

B Images with labels

C Text with sentiment labels

D Unlabeled text

Training needs text data labeled with sentiments like positive or negative.

Explain how a simple sentiment analysis model works from input text to output prediction.

Think about how text is changed into numbers and then processed to guess feelings.

Describe why accuracy is important in evaluating a sentiment analysis model and what it tells us.

Accuracy is about how often the model is right.

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