Domain-specific sentiment in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the domain-specific sentiment dataset.

NLP

import pandas as pd
data = pd.read_csv('[1]')

Drag options to blanks, or click blank then click option'

Ageneral_sentiment.csv

Bdomain_sentiment.csv

Crandom_data.csv

Duser_reviews.csv

Attempts:

3 left

💡 Hint

Common Mistakes

Loading a general sentiment dataset instead of domain-specific.

Using a file name that does not exist.

The domain-specific sentiment dataset is stored in 'domain_sentiment.csv'. Loading this file ensures the model learns from relevant data.

2fill in blank

medium

Complete the code to split the dataset into features and labels.

NLP

X = data['[1]']
y = data['sentiment']

Drag options to blanks, or click blank then click option'

Atext

Blabel

Cscore

Dreview

Attempts:

3 left

💡 Hint

Common Mistakes

Using the label column as features.

Choosing a column unrelated to text.

The feature column containing the text data is named 'text'. This is used as input for the model.

3fill in blank

hard

Fix the error in the code to vectorize the text data using TF-IDF.

NLP

from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer()
X_vectorized = vectorizer.[1](X)

Drag options to blanks, or click blank then click option'

Afit

Btransform

Cfit_transform

Dvectorize

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'transform' without fitting first causes errors.

Using 'fit' alone returns the vectorizer, not transformed data.

To convert raw text into TF-IDF features, use 'fit_transform' which fits the vectorizer and transforms the data in one step.

4fill in blank

hard

Fill both blanks to train a logistic regression model and predict sentiments.

NLP

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.[1](X_vectorized, y)
predictions = model.[2](X_vectorized)

Drag options to blanks, or click blank then click option'

Afit

Bpredict

Ctransform

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'score' instead of 'predict' to get predictions.

Trying to 'transform' data with the model.

First, train the model using 'fit'. Then, use 'predict' to get sentiment predictions on the data.

5fill in blank

hard

Fill all three blanks to compute accuracy, precision, and recall for the model.

NLP

from sklearn.metrics import [1], [2], [3]
accuracy = accuracy_score(y, predictions)
precision = precision_score(y, predictions, average='macro')
recall = recall_score(y, predictions, average='macro')

Drag options to blanks, or click blank then click option'

Aaccuracy_score

Bprecision_score

Crecall_score

Df1_score

Attempts:

3 left

💡 Hint

Common Mistakes

Importing 'f1_score' instead of one of the required metrics.

Forgetting to import all three metrics.

To evaluate the model, import 'accuracy_score', 'precision_score', and 'recall_score' from sklearn.metrics.

Practice

1. What is the main advantage of using domain-specific sentiment analysis over general sentiment analysis?

easy

A. It works for all topics equally well.

B. It requires no training data.

C. It ignores the context of words.

D. It understands feelings better in a specific area.

Solution

1. Step 1: Understand domain-specific sentiment

   Domain-specific sentiment focuses on feelings related to a particular topic or area, making it more precise.

2. Step 2: Compare with general sentiment

   General sentiment tries to work on all topics but may miss nuances in specialized areas.

3. Final Answer:

   It understands feelings better in a specific area. -> Option D

4. Quick Check:

   Domain focus improves understanding = C [OK]

Hint: Domain-specific means better feelings understanding in one area [OK]

Common Mistakes:

• Thinking it needs no training data
• Assuming it works equally well everywhere
• Believing it ignores word context

2. Which of the following is the correct way to prepare data for domain-specific sentiment training?

easy

A. Collect labeled data from the target domain.

B. Train on unlabeled data from a different domain.

C. Use only positive reviews from all domains.

D. Use random text from any topic without labels.

Solution

1. Step 1: Identify training data needs

   Domain-specific sentiment requires labeled examples from the target domain to learn correctly.

2. Step 2: Evaluate options

   Only collecting labeled data from the target domain provides labeled examples from the correct domain, which is essential for training.

3. Final Answer:

   Collect labeled data from the target domain. -> Option A

4. Quick Check:

   Labeled target data needed = D [OK]

Hint: Training needs labeled data from the right domain [OK]

Common Mistakes:

• Using unlabeled or random data
• Mixing data from unrelated domains
• Ignoring the need for labels

3. 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?

medium

A. [1]

B. [0]

C. Error due to missing labels

D. [1, 0]

Solution

1. Step 1: Understand training data and labels

   The model is trained on positive and negative examples related to product features.

2. 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.

3. Final Answer:

   [1] -> Option A

4. Quick Check:

   Similar positive text predicts 1 = A [OK]

Hint: New text similar to positive training predicts positive [OK]

Common Mistakes:

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

4. 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?

medium

A. Labels are reversed in training data.

B. The vectorizer was not fit before transform.

C. The model was trained on too few examples.

D. The new text was not transformed correctly.

Solution

1. Step 1: Check training data size

   Only two examples are used, which is too small for the model to learn properly.

2. Step 2: Analyze model behavior

   With limited data, the model may predict the majority class or fail to distinguish negative phrases.

3. Final Answer:

   The model was trained on too few examples. -> Option C

4. Quick Check:

   Small training data causes poor predictions = A [OK]

Hint: Too few training examples cause wrong predictions [OK]

Common Mistakes:

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

5. You want to improve domain-specific sentiment analysis for movie reviews. Which approach best combines domain knowledge and model accuracy?

hard

A. Train a sentiment model on general tweets and apply it to movie reviews.

B. Collect labeled movie reviews, fine-tune a pre-trained language model, and test on movie data.

C. Use a dictionary of positive and negative words from unrelated domains.

D. Train a model only on unlabeled movie reviews using clustering.

Solution

1. Step 1: Identify domain-specific data needs

   Using labeled movie reviews ensures the model learns relevant sentiment patterns.

2. Step 2: Use advanced model fine-tuning

   Fine-tuning a pre-trained language model adapts general knowledge to the movie domain, improving accuracy.

3. Final Answer:

   Collect labeled movie reviews, fine-tune a pre-trained language model, and test on movie data. -> Option B

4. Quick Check:

   Labeled domain data + fine-tuning = best accuracy [OK]

Hint: Fine-tune with labeled domain data for best results [OK]

Common Mistakes:

• Using unrelated domain data only
• Relying on unlabeled data without supervision
• Using generic word lists without context