Topical tag cloud generator for researchers/academics I've seen a few academic homepages with tag clouds indicating their topics near the top of the page. I quite like the idea... it gives a nice simple/quick overview of the topics the person is interested in.

Thus I was looking for a tool to (help) generate a tag cloud of my own topics. I'm not looking for a generic tag cloud generator. Rather the tool should take a list of PDFs of papers, extract the topical keywords and then generate the tag cloud. Ideally the tool should be clever enough to rule out noise or common stop-words like "related work", "funded by", and so on.

Alternatively, multiple tools could be used: e.g., one to do the extraction of weighted topics from the papers and another one to generate the image.



Just to pull a nice looking example from some random topic searches on Google images ("tag cloud logic programming" in this case):

 

Source.



EDIT: Why can I not use a generic tag cloud generator like Wordle? 

If I use Wordle or generic tag cloud generators, I get "et", "al", "logic", "programming", "figure", "table". I can filter stop-words manually but I still get single words with no n-gram detection ("logic" and "programming" do not convey "logic programming"). 

On the other hand, there are tools for topic extraction that will intelligently extract n-gram topics from PDFs (using k-means, gazateers, tf-idf, that sort of stuff ... I'm not an expert). However, these are typically not weighted for sizing in tag clouds. Hence it seems you need a tool that connects/does both.
As I understand the main issues are:


how to extract text from publications,
how to extract disciplines/subjects from the text.


In the first case, following @ff524's comment, see Creating a word cloud from PDF documents. In general, you can try to search for pdf to text conversion (as PDF is a graphical format, sometimes it is tricky to get all content in the exact order, but for this application it should work fine).

When it comes to topic extraction, and elimination of common words, you need to compare your n-grams (i.e. sequences of n words) to some other dataset (for the one by Google). But maybe you need to compare it to n-grams created from scientific words (to eliminate see Fig, et al., previous works).

However, even carefully looking at frequencies is not enough to eliminate all "common words". For example, if you paper discipline-specific words or expressions, it may be hard to distinguish them from discipline names. And, depending on your taste, you may or may not want to include them.

To extract disciplines you need to have some list of it, so you can compare with it. Some time ago I was collecting one (see this gist). One trick to make one is to look at Wikipedia for articles related to your branch.

If you are more in natural language processing, you can look at:


P. Buitelaar, T. Eigner, Topic Extraction from Scientific Literature for Competency Management


or search for similar publications.

For tag cloud visualization you can use http://www.wordle.net/ or something similar. But beware, some people are highly opinionated against tag clouds.

SIDE REMARK:

Personally, I think that graphs are a better tool to convey information, as similar concepts are clustered together (in opposition to having a soup of concepts). See things in line of e.g. TagOverflow or maps of SE (disclaimer: these are my projects).
Once you get tags and some quantity relating them, it is easy to make a nice graph with Gephi.