Why topic modeling discovers themes in NLP - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library used for topic modeling.

NLP

from sklearn.decomposition import [1]

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

APCA

BLinearRegression

CKMeans

DNMF

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing PCA which is for dimensionality reduction but not typically for topic modeling.

Choosing KMeans which is a clustering algorithm, not matrix factorization.

The NMF class from sklearn.decomposition is commonly used for topic modeling to discover themes in text data.

2fill in blank

medium

Complete the code to fit the topic model on the document-term matrix.

NLP

model = NMF(n_components=5, random_state=42)
W = model.[1](X)

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Atransform

Bfit_transform

Cfit_predict

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform without fitting first causes an error.

fit_predict is not a method of NMF.

The fit_transform method fits the model and returns the transformed data (topic weights for documents).

3fill in blank

hard

Fix the error in printing the top words for each topic.

NLP

feature_names = vectorizer.get_feature_names_out()
for topic_idx, topic in enumerate(model.components_):
    print(f"Topic {topic_idx}:", end=" ")
    print(" ".join([feature_names[i] for i in topic.argsort()[1]()[:10]]))

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A::-1

Bascending

Creverse

Dsort

Attempts:

3 left

💡 Hint

Common Mistakes

Using sort() which returns None and causes an error.

Using reverse() which works only on lists, not numpy arrays.

Using argsort()[::-1] sorts indices in descending order to get top words per topic.

4fill in blank

hard

Fill both blanks to create a dictionary of topics with their top words.

NLP

top_words = {f"Topic_{i}": [feature_names[j] for j in model.components_[i].[1]()[2][:5]] for i in range(model.n_components)}

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Aargsort()[::-1]

Bsort

Creverse

Dargsort

Attempts:

3 left

💡 Hint

Common Mistakes

Using sort which returns None.

Trying to use reverse which is not valid here.

We use argsort()[::-1] to get indices of top words in descending order. The second blank is empty because slicing is done after reversing.

5fill in blank

hard

Fill all three blanks to compute and print the coherence score of the topic model.

NLP

from gensim.models import CoherenceModel
texts = [[word for word in doc.split()] for doc in documents]
coherence_model = CoherenceModel(model=model, texts=texts, dictionary=[1], coherence=[2])
coherence_score = coherence_model.[3]()
print(f"Coherence Score: {coherence_score:.3f}")

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Adictionary

Bcorpus

Cget_coherence

Dc_v

Attempts:

3 left

💡 Hint

Common Mistakes

Passing corpus instead of dictionary.

Using wrong method name to get coherence.

The dictionary is needed for coherence calculation, c_v is a common coherence type, and get_coherence() computes the score.

Practice

1. Why does topic modeling help discover themes in a collection of documents?

easy

A. Because it groups words that often appear together, revealing common ideas

B. Because it translates documents into different languages

C. Because it counts the number of sentences in each document

D. Because it removes all stop words from the text

Solution

1. Step 1: Understand the goal of topic modeling

   Topic modeling aims to find hidden themes by grouping words that frequently appear together in documents.

2. Step 2: Recognize how grouping words reveals themes

   Words that co-occur often represent a shared idea or theme, so grouping them helps discover these themes.

3. Final Answer:

   Because it groups words that often appear together, revealing common ideas -> Option A

4. Quick Check:

   Grouping co-occurring words = Discover themes [OK]

Hint: Topic modeling groups co-occurring words to find themes [OK]

Common Mistakes:

• Thinking topic modeling translates text
• Confusing word counts with sentence counts
• Believing stop word removal finds themes

2. Which of the following is the correct way to represent documents for Latent Dirichlet Allocation (LDA)?

easy

A. A sequence of document titles only

B. A matrix of word counts per document

C. A list of document lengths in characters

D. A set of document publication dates

Solution

1. Step 1: Recall LDA input format

   LDA requires a matrix where each row is a document and each column is a word count, showing how often each word appears in each document.

2. Step 2: Eliminate incorrect options

   Document lengths, titles, or dates do not provide word frequency information needed for LDA.

3. Final Answer:

   A matrix of word counts per document -> Option B

4. Quick Check:

   LDA input = word count matrix [OK]

Hint: LDA uses word count matrices as input [OK]

Common Mistakes:

• Using document titles instead of word counts
• Confusing document length with word frequency
• Including metadata like dates as input

3. Given the following simplified topic-word distribution from LDA:
Topic 1: {"apple": 0.4, "banana": 0.3, "fruit": 0.3}
Topic 2: {"car": 0.5, "engine": 0.3, "wheel": 0.2}
Which theme does Topic 1 most likely represent?

medium

A. Vehicles and parts

B. Sports equipment

C. Technology gadgets

D. Fruits and food

Solution

1. Step 1: Analyze the top words in Topic 1

   Words like "apple", "banana", and "fruit" are all related to food, specifically fruits.

2. Step 2: Match words to a theme

   These words clearly indicate the theme is about fruits and food, not vehicles, technology, or sports.

3. Final Answer:

   Fruits and food -> Option D

4. Quick Check:

   Topic words = Fruits theme [OK]

Hint: Top words reveal the theme quickly [OK]

Common Mistakes:

• Confusing 'apple' as a tech brand only
• Ignoring the presence of 'fruit' word
• Mixing topics with unrelated themes

4. You run LDA on a set of documents but get topics that mix unrelated words like 'apple' and 'engine' together. What is the most likely cause?

medium

A. The documents were not preprocessed to remove stop words and noise

B. The number of topics chosen is too high

C. The word counts matrix was sorted alphabetically

D. The documents are too short to find any topics

Solution

1. Step 1: Understand the effect of preprocessing

   Without removing stop words and noise, unrelated words can appear together, confusing the model.

2. Step 2: Evaluate other options

   Too many topics usually separate words more; sorting word counts does not affect modeling; short documents may reduce quality but not cause mixed unrelated words.

3. Final Answer:

   The documents were not preprocessed to remove stop words and noise -> Option A

4. Quick Check:

   Preprocessing needed to avoid mixed topics [OK]

Hint: Always preprocess text before topic modeling [OK]

Common Mistakes:

• Blaming topic number without checking preprocessing
• Thinking sorting affects topic quality
• Assuming short documents cause unrelated word mixing

5. You want to discover themes in a large set of customer reviews using topic modeling. Which approach will best help interpret the discovered topics?

hard

A. Sort reviews by length before modeling

B. Count the total number of words in all reviews

C. Look at the top words in each topic to understand the main ideas

D. Use only the first sentence of each review for modeling

Solution

1. Step 1: Understand how to interpret topics

   Topic modeling outputs topics as groups of words with probabilities. The top words show the main ideas of each topic.

2. Step 2: Evaluate other options

   Counting words or sorting reviews does not help interpret themes. Using only first sentences loses information.

3. Final Answer:

   Look at the top words in each topic to understand the main ideas -> Option C

4. Quick Check:

   Top words reveal topic meaning [OK]

Hint: Top words explain topic themes clearly [OK]

Common Mistakes:

• Ignoring top words for interpretation
• Focusing on review length instead of content
• Using incomplete text for modeling