Predictive Analytics with TensorFlow 6.1: NLP Analytics Pipelines

The video tutorial introduces NLP analytics pipelines, highlighting the workflow from data ingestion to predictive analytics. It emphasizes the importance of feature engineering for unstructured text and discusses challenges faced by MLP models in interpreting semantics. The tutorial explores text analytics techniques like sentiment analysis, topic modeling, TF-IDF, named entity recognition, and event extraction, illustrating their applications in uncovering patterns and relationships in text data.

What is the first step in a general machine learning predictive analytics workflow?

• Data cleansing

• Feature extraction

• Data ingestion

• Model evaluation

Why might MLP models struggle with understanding complex sentences?

• They have limited vocabulary size

• They are not designed for text data

• They often fail to interpret semantics

• They require too much computational power

Which technique is used to detect topics or themes in a corpus of documents?

• Named entity recognition

• Sentiment analysis

• Topic modeling

• Event extraction

What does TF-IDF measure in text analytics?

• The sentiment of a document

• The grammatical structure of sentences

• The frequency of words in a document

• The relationship between entities

What is the purpose of named entity recognition?

• To measure word frequency

• To detect events in text

• To extract information about entities like persons and locations

• To analyze political opinions