[Solved] You have this code snippet for domain-specific sentiment training:texts = ['Good food', 'Bad service'] labels = [1, 0] vectorizer = CountVectorizer() X = vectorizer.... | NLP

NLP - Sentiment Analysis Advanced

You have this code snippet for domain-specific sentiment training:

texts = ['Good food', 'Bad service']
labels = [1, 0]
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)
model = LogisticRegression()
model.fit(X, labels)

new_text = ['Bad food']
X_new = vectorizer.transform(new_text)
pred = model.predict(X_new)
print(pred)

The output is always [1] even for negative phrases. What is the likely error?

ALabels are reversed in training data.

BThe vectorizer was not fit before transform.

CThe model was trained on too few examples.

DThe new text was not transformed correctly.

Step-by-Step Solution

Solution:

Step 1: Check training data size
Only two examples are used, which is too small for the model to learn properly.
Step 2: Analyze model behavior
With limited data, the model may predict the majority class or fail to distinguish negative phrases.
Final Answer:
The model was trained on too few examples. -> Option C
Quick Check:
Small training data causes poor predictions = A [OK]

Quick Trick: Too few training examples cause wrong predictions [OK]

Common Mistakes:

MISTAKES

Assuming vectorizer not fit causes this
Thinking labels are reversed
Believing transform step is incorrect

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You have this code snippet for domain-specific sentiment training:

Step 1: Check training data size

Step 2: Analyze model behavior

Final Answer:

Quick Check:

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