Which algorithms are best suited to this scenario?

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A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings.The company stores audit documents in text format.Auditors have requested help from a data science team to quickly analyze the documents.The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members.Documents that describe adverse events must receive the highest priority.A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic.

Which algorithms are best suited to this scenario?

(Choose two.)

Latent Dirichlet allocation (LDA)

Random forest classifier

Neural topic modeling (NTM)

Linear support vector machine

Linear regression

Explanations:

Latent Dirichlet Allocation (LDA) is a well-known statistical model for topic modeling, which identifies abstract topics in large collections of text data. It is well-suited for discovering topics in audit documents.

Random Forest classifier is a supervised learning algorithm used for classification or regression, not for discovering topics in text. It doesn’t model topics in an unsupervised manner.

Neural Topic Modeling (NTM) is another advanced approach for topic discovery, leveraging deep learning techniques to learn more complex and richer topics, making it suitable for the task.

A Linear Support Vector Machine (SVM) is a supervised classifier, not an unsupervised algorithm for topic modeling. It would require labeled data to classify documents, which is not the case here.

Linear Regression is a regression model and is not used for topic modeling. It is designed for predicting continuous variables, not for extracting abstract topics from text.

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