A tip for submitting to DMOZ — make your site description clear
I just picked out a few of the many unreviewed sites in my DMOZ categories to evaluate, and listed most of those I reviewed.
How did I choose them to get screened? Mainly, I picked out ones with focused descriptions, titles, and so on, that just seemed likely to be listable based on that info (which is the essence of what I see on the page where all the various submitted sites are linked). I correctly guessed that I’d be able to quickly understand what I was seeing and judge whether to list the site or not, quickly write the official site description, and so on. Read more
| Categories: Categorization and filtering, Directories, ODP and DMOZ, Search engine optimization (SEO) | 2 Comments |
Predictive analytics vendors’ text mining sophistication
Steve Gallant of KXEN contacted me over the summer to show me KXEN’s new text mining capability. It was pretty basic bag-of-words stuff, which is still a lot better than nothing, and actually fits pretty well with KXEN’s general simplicity-centric strategy.
This inspired me to check whether there had been any big changes in text mining capabilities at SAS or SPSS. It turned out there hadn’t. SAS is also still on the bag-of-words level. SPSS, however, does do sentiment analysis (pretty obvious, considering their focus on surveys and the like) and negation.
Thanks go out to Mary Crissey and Olivier Jouve for getting back to me when I asked, along with apologies for taking a while to post what they told me.
| Categories: SAS, SPSS, Sentiment analysis, Text mining | Leave a Comment |
