Course talk:CPSC522/A Comparison of LDA and NMF for Topic Modeling on Literary Themes

Sorry, I totally forgot to submit this critique!

This is an interesting project, and you got some neat results. There's some opportunity for more rigorous analysis, e.g. by having other people look over the given topic bundles and rate their cohesiveness, and maybe try to name the themes! Probably out of scope for this type of course project. The discussion was fairly strong.

It's great to see some explanatory pseudocode (and code)

• The topic is relevant for the course. 5
• The writing is clear and the English is good. 4
• The page is written at an appropriate level for CPSC 522 students (where the students have diverse backgrounds). 5
• The formalism (definitions, mathematics) was well chosen to make the page easier to understand. 5
• The abstract is a concise and clear summary. 5
• There were appropriate (original) examples that helped make the topic clear. 5
• There was appropriate use of (pseudo-) code. 5
• It had a good coverage of representations, semantics, inference and learning (as appropriate for the topic). 5
• It is correct. 5
• It was neither too short nor too long for the topic. 4 could've expanded
• It was an appropriate unit for a page (it shouldn't be split into different topics or merged with another page). 5
• It links to appropriate other pages in the wiki. 5
• The references and links to external pages are well chosen. 5
• I would recommend this page to someone who wanted to find out about the topic. 5
• This page should be highlighted as an exemplary page for others to emulate. 4

If I was grading it out of 20, I would give it: 18

Comparison_between_LDA_and_NMF

Comments

No complaints here, looks nice and is written in a clear and understandable manner. One minor thing: Your hypothesis (or rather motivation) is kind of short. Why are you comparing these two approaches? And what other current work is there in scientific literature? Surely, people must have gone further than LDA and matrix factorization in the field of topic inference.

Marking Scheme

I a scale of 1 to 5, where 1 means "strongly disagree" and 5 means "strongly agree" please rate and comment on the following:

   The topic is relevant for the course. 5
   The writing is clear and the English is good. 5
   The page is written at an appropriate level for CPSC 522 students (where the students have diverse backgrounds). 5
   The formalism (definitions, mathematics) was well chosen to make the page easier to understand. 5
   The abstract is a concise and clear summary. 5 
   There were appropriate (original) examples that helped make the topic clear. 5
   There was appropriate use of (pseudo-) code. 5
   It had a good coverage of representations, semantics, inference and learning (as appropriate for the topic). 5
   It is correct.5
   It was neither too short nor too long for the topic. 5
   It was an appropriate unit for a page (it shouldn't be split into different topics or merged with another page). 5
   It links to appropriate other pages in the wiki. 5
   The references and links to external pages are well chosen. 5
   I would recommend this page to someone who wanted to find out about the topic. 5
   This page should be highlighted as an exemplary page for others to emulate. 4

If I was grading it out of 20, I would give it: 19

Thank you for the critique, Fabian. :)

Overall a nice article covering the two methodologies. Some of my suggestions are:

1. I felt LDA by itself needed a better explanation. Maybe give a mathematic formulation for LDA.
2. Some latex words don't appear aesthetically appealing (like tf idf vectorizer). Maybe a workaround?
3. I felt the conclusion needs to be a bit more substantial but nonetheless follows from the experiments.

I would grade it 18 out of 20.

Thank you for the feedback, Kevin!