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"So an automated tool that crawls VC websites, pulls the links to each and every portfolio company, categorizes these investments by stage, sector, geography, a

"So an automated tool that crawls VC websites, pulls the links to each and every portfolio company, categorizes these investments by stage, sector, geography, and ideally a host of other things, would be incredibly useful." In theory, not hard. It would be fun to run a LDA model and see what topics come up. In practice, hard. Given that every VC website probably presents this info in slightly different ways, not to mention the variety in the portfolio company websites, not to mention the survivorhip bias (where is the info on the portfolio companies which failed)... But if we hired a person to gather and structure all of this data... seriously, mechanical turks have their uses.

lizthegrey 12y ago
This sounds exactly like what Mattermark is already doing.
ganeumann 12y ago
I built the scraper part of this tool about four years ago: http://neuvc.com/labs/vcdelta http://neuvc.com/labs/vcdelta or https://twitter.com/vcdelta https://twitter.com/vcdelta, for daily feed It wasn't in practice that hard to set up, it's a scraper in python with a DSL to tell it how each VC site set up their portfolio page. The pain is maintaining it. Every week a few of the scrapers break because the VC changes their site. And recently, with so many seed funds starting up, it's missing a lot of the interesting new VCs. [I once scraped Crunchbase for each VC firm, and the descriptions of every company they invested in, ran them all through a naive bayes model, and set it up so that you could type in your company description and it would tell you which firm was most likely to invest in you. It didn't work at all, giving me new appreciation for people who actually know how to make ML models work.]
deleted 12y ago
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villu 12y ago
You may want to give Funderbeam (http://funderbeam.com http://funderbeam.com) a shot -- We have data on 30k+ investors of various kinds. With their portfolio investments, target geographies, and funding sizes. More filters on the way.

lizthegrey 12y ago

This sounds exactly like what Mattermark is already doing.

ganeumann 12y ago

I built the scraper part of this tool about four years ago: http://neuvc.com/labs/vcdelta http://neuvc.com/labs/vcdelta or https://twitter.com/vcdelta https://twitter.com/vcdelta, for daily feed It wasn't in practice that hard to set up, it's a scraper in python with a DSL to tell it how each VC site set up their portfolio page. The pain is maintaining it. Every week a few of the scrapers break because the VC changes their site. And recently, with so many seed funds starting up, it's missing a lot of the interesting new VCs. [I once scraped Crunchbase for each VC firm, and the descriptions of every company they invested in, ran them all through a naive bayes model, and set it up so that you could type in your company description and it would tell you which firm was most likely to invest in you. It didn't work at all, giving me new appreciation for people who actually know how to make ML models work.]

deleted 12y ago

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villu 12y ago

You may want to give Funderbeam (http://funderbeam.com http://funderbeam.com) a shot -- We have data on 30k+ investors of various kinds. With their portfolio investments, target geographies, and funding sizes. More filters on the way.