Topic Modeling Week 5

Explain the process of LDA for topic modeling.

• LDA stands for Long Distance Association and is used in telecommunications

• LDA is used to identify topics within a collection of documents by assigning probability distributions to words and documents.

• LDA is a programming language commonly used for sentiment identification and web development

• LDA is a type of computer hardware used for data processing

How does LDA help in identifying topics within a set of documents?

• LDA uses a probabilistic model to assign words to topics and then assign topics to documents based on the distribution of words.

• LDA uses a deterministic model to assign words to topics

• LDA does not consider the distribution of words in assigning topics to documents

• LDA is not suitable for identifying topics within a set of documents

Which one is the TRUE statement for LDA in topic modeling?

• LDA can handle large datasets easily

• LDA includes difficulty in the inability to capture word dependencies

• LDA is not sensitive to the number of topics chosen

• LDA captures word dependencies accurately

How can topic modeling be used in the field of natural language processing?

• Topic modeling can be used to automatically discover topics in text documents

• Topic modeling can be used to predict weather patterns

• Topic modeling can be used to analyze financial data

• Topic modeling can be used to design computer algorithms

What are some popular algorithms used for topic modeling other than LDA?

• Decision Trees

• K-means clustering

• Support Vector Machines (SVM)

• Latent Semantic Analysis (LSA)

What common evaluation metric is used for assessing the performance of topic modeling algorithms?

• Mean Squared Error (MSE)

• Coherence Score

• Root Mean Square Error (RMSE)

• Accuracy