The video tutorial covers the concept of TF-IDF, a method used in text analysis to determine the importance of words in documents. It explains how to compute term frequency (TF) and inverse document frequency (IDF), and how to implement TF-IDF in Python using user-defined functions and the SKLearn library. The tutorial also demonstrates how to improve predictive models with TF-IDF and NLTK, and discusses training and testing models with TF-IDF features. Finally, it introduces the next topic of using word2vec for sentiment analysis.

What does TF-IDF stand for in the context of document analysis?

• Term Frequency-Inverse Document Frequency

• Text Frequency-Index Document Frequency

• Text Factor-Inverse Data Frequency

• Term Factor-Index Document Factor

How is the term frequency (TF) of a word in a document calculated?

• By subtracting the word's frequency from the total number of words

• By multiplying the word's frequency by the document length

• By dividing the number of documents by the number of words

• By counting the number of times the word appears in the document

What is the purpose of the inverse document frequency (IDF) in TF-IDF?

• To decrease the importance of rare words

• To increase the frequency of common words

• To reflect how common or rare a word is across documents

• To calculate the total number of words in a document

Which Python module is mentioned as providing a TF-IDF vectorizer?

• Matplotlib

• NumPy

• Pandas

• SK learn

What is the role of the NLTK module in the context of TF-IDF?

• To perform mathematical operations

• To provide pre-trained tokenizer models

• To manage file input and output

• To visualize data

Why is it unnecessary to use one-hot encoding with TF-IDF?

• Because TF-IDF already provides a sparse feature vector

• Because TF-IDF is only used for numerical data

• Because one-hot encoding is too complex

• Because one-hot encoding is outdated

What might cause a predictive model to perform worse on a test set compared to a training set?

• The test set uses a different language

• The test set is not preprocessed

• The test set has fewer words than the training set

• The test set has more words than the training set