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Topic modeling in business and marketing Flashcards | StudBud
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Topic modeling in business and marketing
Topic modeling in business and marketing
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1
What is a common preprocessing step for texts before applying topic modeling?
Adding more features to the text
Increasing the text data's length
Removing stopwords
Converting text to audio
2
What does “latent” refer to in Latent Dirichlet Allocation (LDA)?
The number of topics to be extracted
Obvious and observable topics in the data
The most important topics in the data
Hidden or unobservable topics in the data
3
In marketing, why is topic modeling particularly useful for social media analysis?
It can identify trending topics
It can analyze traditional media campaigns
It is not useful for social media analysis
It can automatically generate viral content
4
Which of the following is NOT a common application of topic modeling in research?
Customer segmentation
Price optimization
Content recommendation
Employee payroll management
5
What is a drawback of Latent Dirichlet Allocation (LDA) when applied to business text data?
It requires labeled training data
It cannot handle large datasets
It is computationally demanding
It doesn't provide an interpretation of topics
6
In a topic model, what is a "document-topic distribution"?
The length of a document in terms of words
A list of keywords associated with a document
The sentiment score of a document
The probability distribution of topics in a given document
7
What is the primary purpose of using topic models in research?
To identify and extract meaningful patterns in text data
To optimize supply chain management
To improve customer service
To enhance product design
8
In topic modeling, what is a "topic" typically defined as?
A collection of words or phrases that represent a theme
A demographic group
A product description
A single keyword
9
Which of the following algorithms is commonly used for topic modeling in business applications?
K-means clustering
Latent Dirichlet Allocation (LDA)
Principal Component Analysis (PCA)
Support Vector Machines (SVM)
10
How can businesses use topic models in content recommendation systems?
By recommending content only from the same source
By recommending content unrelated to the user's interests
By suggesting relevant content based on identified topics
By ignoring user preferences altogether
11
How can businesses benefit from topic modeling in customer feedback analysis?
Identify market trends and competitors
Enhance financial forecasting accuracy
Segment customers based on preferences and concerns
Improve product quality and performance
12
What is the term for a word that frequently appears within a topic but is not exclusive to that topic?
Stopword
Anchor word
Synonym
Outlier
