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TherePlease refer to: [[Amazon Mechanical Turk for Analyzing Demo Day Classifier's a file in the folder CrawledHTMLFullcalledResults]] FinalResultWithURLthat was manually created by combining the file crawled_demoday_page_list.txtin the mother folder and the file predicted.txtThis file combined the predictions to the actual url of the websites. ==Hand Collecting Data==
Since MTurk makes it hard To crawl, we only looked for us data on accelerators which did not receive venture capital data (which Ed found via VentureXpert) and lacked timing info. The purpose of this crawl is to display the downloaded HTMLfind timing info where we cannot find it otherwise, it and if a company received VC we can find timing info via that investment. The file we used to find instances in which we lack timing info and lacked VC is much faster to just copy the url into the question box rather than trying to display the downloaded HTML: /bulk/McNair/Projects/Accelerators/Summer 2018/Merged W Crunchbase Data as of July 17.xlsx
The advantage to We filtered this is sheet in Excel (and checked our work by filtering in SQL) and found 809 companies that some websites, such as techcrunchlacked timing info and didn't receive VC.com behaves abnormally when downloaded as HTML so opening these kinds of websites in the browser would actually be more beneficial because the UI would not be messed up. MoreoverFrom this, if certain websites has paywall or pop-up ads, the user can also click out of it. Since most of the times, paywall or pop-ups are scripts within HTMLs, the classifier can't rule them out because the body of the HTMLs may still contain useful information we are looking found 74 accelerators which we needed to crawl for. Major paywalls or websites that required log-ins such as linkedin have been black-listed in the crawler. More detail in the crawler section below.
However. there is a disadvantage to this: websites are ever changing, so there is a possibility that in the future, the URL may not be usable, or has changed to something else; on We used the other hand, downloaded HTMLs remain the same because it does not require any internet connection crawler to render and thus, the content is staticsearch for cohort companies listed for these accelerators.
To create During the MTurk for this projectinitial test run, follow this tutorial in [[Mechanical Turk (Tool)]]. For testing and development purpose, use https://requestersandboxthe number of good pages was 359.mturkThe data is then handled by hand by fellow interns.com/
Test accountThe file for hand-coding is in:email: mcboatfaceboaty670@gmail.compassword: sameastheoneforemail2018 /bulk/McNair/Projects/Accelerator Demo Day/Test Run/CrawledDemoDayHTMLFull/'''FinalResultWithURL'''
*Whether We split the page had a list of companies going through an accelerator*The month and year of process into four parts. Each interns will do the demo day (or article)*Accelerator name*Companies going through acceleratorfollowing:
Layout:1. Go to the given URL. 2. Record whether the page is good data (column F); this can later be used by [[Minh Le]] to refine/fine-tune training data. 3. Record whether the page is announcing a cohort or recapping/explaining a demo day (column G). This variable will be used to decide if we should subtract weeks from the given date (e.g. if it is recapping a demo day, the cohort went through the accelerator for the past ~12 weeks, and we should subtract weeks as such). 4. Record date, month, year, and the companies listed for that given accelerator. 5. Note any any information, such as a cohort's special name. Once this process is finished, we will filter only the 1s in Column F, and [[Connor Rothschild]] and [[Maxine Tao]] will work to populate empty cells in The File to Rule Them All with that data.
[[File:Screen Shot 2018-07-25 at 11.37.02 AM.png]]
Accelerator Demo Day (view source)
Revision as of 13:47, 21 September 2020
, 13:47, 21 September 2020no edit summary
{{Project|Has project output=Tool|Has sponsor=McNair ProjectsCenter
|Has title=Accelerator Demo Day
|Has owner=Minh Le,
The RNN is still under much development. Modifying anything in this folder is not recommended
All the other folders are used for experimenting purposes, please don't touch them. If you want to understand more about the files as a general user, go to the section A Quick Glance through the File in The Directory below. If you are a developer, go to the Advance User Guide section.
==General User Guide: How to Use this Project (Random Forest model)==
NEVER touch the TrainingHTML folder, datareader.py or the classifier.txt. These are used internally to train data.
==A Quick Glance through the File in The Directory==
All working file is stored in this folder:
E:\McNair\Projects\Accelerator Demo Day\Test Run
The file
==Amazon Mechanical Turk==
For this project, all the fields that was asked sake of collaboration, the user isteam copied this information to a Google Sheet, accessible here: https://docs.google.com/spreadsheets/d/16Suyp364lMkmUuUmK2dy_9MeSoS1X4DfFl3dYYDGPT4/edit?usp=sharing
==Advance User Guide: An in-depth look into the project and the various settings==
===Accelerators needed to Crawl===The name lists of Accelerators to crawl is stored in the file: E:\McNair\Projects\Accelerator Demo Day\Test Run\ListOfAccsToCrawl.txt ===Training Data===Training data is stored in the folder: E:\McNair\Projects\Accelerator Demo Day\Test Run\TrainingHTML ===The Crawler Functionality===
The crawler functionality is stored in the file:
STEP1_crawl.py
Because apparently for some reason it stopped grabbing the first web page (I think because google may have modified how their website looks.
===The Classifier===
===Input (Features)===
The RNN currently has a ~50% accuracy on both train and est data, which is rather concerning.
Test : train ration ratio is 1:3 (25/75)
Both model is currently using the Bag-of-word approach to preprocess data, but I will try to use Yang's code in the industry classifier to preprocess using word2vec. I'm not familiar with this approach, but I will try to learn this.
