1)

For each problem below, determine whether Text Classification or Topic Analysis is more appropriate to solve it.

Group of answer choices

1. Partition the reviews of a companys flagship product into homogenous clusters

[ Choose ] Text Classification Topic Analysis

2. Automatically label an email as work-related or not

[ Choose ] Text Classification Topic Analysis

3. Predicting whether an essay written by a college applicant if of low quality or high quality

[ Choose ] Text Classification Topic Analysis

4. Given a set of essays written by college applicants, group them by topic

[ Choose ] Text Classification Topic Analysis

2) In the context of topic analysis, which matrix is the most appropriate to describe each topic?

The topic-term matrix

The document-term matrix

The document-topic matrix

3)

Among the classification tasks below, for which is logistic regression better suited than Nave Bayes?

Group of answer choices

Find which tweets are written by a native English speaker vs a non-native English speaker

Classify emails as sad, happy, or neutral

Classify emails as important or not important

Predict which emails are spam

4)

In Wikipedia, an edit-war is said to take place when two contributors revert each others edits multiple times. Here is an example of edit war:

An Edit War

Version	Text
Version 1	The tree is tall
Version 2	The shrub is short
Version 3	The tree is tall
Version 4	The shrub is short

Complete the following edit matrix corresponding to the edit war above. The first row has already been completed. Be sure to enter your answers carefully. Format your numbers as in the following examples: 1, -1, 2, -2, etcetera.

Edit Matrix

	Tree	Shrub	Tall	Short
Edit 1 (ver1ver2)	-1	1	-1	1
Edit 2 (ver2ver3)
Edit 3 (ver3ver4)