[Solved] Given this Python snippet for domain-specific sentiment prediction:from sklearn.feature_extraction.text import CountVectorizer from sklearn.... | NLP

NLP - Sentiment Analysis Advanced

Given this Python snippet for domain-specific sentiment prediction:

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression

texts = ['Great battery life', 'Poor screen quality', 'Excellent camera']
labels = [1, 0, 1]  # 1=positive, 0=negative

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(texts)
model = LogisticRegression()
model.fit(X, labels)

new_text = ['Battery lasts long']
X_new = vectorizer.transform(new_text)
pred = model.predict(X_new)

What is the expected output of pred?

A[1]

B[0]

CError due to missing labels

D[1, 0]

Step-by-Step Solution

Solution:

Step 1: Understand training data and labels
The model is trained on positive and negative examples related to product features.
Step 2: Predict sentiment for new text
'Battery lasts long' is similar to 'Great battery life', which is labeled positive (1), so prediction should be positive.
Final Answer:
[1] -> Option A
Quick Check:
Similar positive text predicts 1 = A [OK]

Quick Trick: New text similar to positive training predicts positive [OK]

Common Mistakes:

MISTAKES

Expecting multiple predictions for single input
Confusing labels or expecting error
Ignoring vectorizer transform step

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Given this Python snippet for domain-specific sentiment prediction:

Step 1: Understand training data and labels

Step 2: Predict sentiment for new text

Final Answer:

Quick Check:

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