Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

- JMP User Community
- :
- Discussions
- :
- How to interpret Topic Loading and Topic Score in Latent Semantic Analysis, SVD ...

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

Highlighted

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Created:
Oct 11, 2019 1:32 PM
| Last Modified: Oct 11, 2019 1:38 PM
(1366 views)

Hi Community,

When you run SVD on Text Explorer it produces Topic Score and Topic Loading. I understand that these are the Singular vectors at document level and at term level respectively. But how do you actually interpret the values of a particular Topic Score or Topic Loading. There are both negative and positive numbers. Does a high positive number mean something as opposed to a high negative number. For example if term1 has high positive value on a Topic 1 and high negative value in Topic 2. Does that mean term1 is high on topic 1 and less on Topic 2. Is the interpretation similar when we have document scores against topics?

1 ACCEPTED SOLUTION

Accepted Solutions

Highlighted

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

The scores are a new representation of the original weighted DTM and the loadings are the correlations of the topics with the original weighted DTM. The larger the magnitude of the scores, the more different documents are from each other and the mean. A large negative loading value shows an important term because it is highly correlated.

Text analysis often refers to terms with the adjectives "attractive" or "repulsive" based on the sign.

Learn it once, use it forever!

2 REPLIES 2

Highlighted

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

The scores are a new representation of the original weighted DTM and the loadings are the correlations of the topics with the original weighted DTM. The larger the magnitude of the scores, the more different documents are from each other and the mean. A large negative loading value shows an important term because it is highly correlated.

Text analysis often refers to terms with the adjectives "attractive" or "repulsive" based on the sign.

Learn it once, use it forever!

Highlighted
##

Thank you very Mark Bailey for taking the time to answer my question. I am still reviewing the results of my data. If I have further data interpretation questions, I will post under this discussion.

- Mark as New
- Bookmark
- Subscribe
- Mute
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Re: How to interpret Topic Loading and Topic Score in Latent Semantic Analysis, SVD in Text Explorer

Article Labels

There are no labels assigned to this post.