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TherePlease refer to: [[Amazon Mechanical Turk for Analyzing Demo Day Classifier's a file in the folder Results]] CrawledHTMLFullcalled==Hand Collecting Data== FinalResultWithURLTo crawl, we only looked for 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 find timing info where we cannot find it otherwise, and if a company received VC we can find timing info via that was manually created by combining the investment. The filewe used to find instances in which we lack timing info and lacked VC is: crawled_demoday_page_list/bulk/McNair/Projects/Accelerators/Summer 2018/Merged W Crunchbase Data as of July 17.txtxlsx We filtered this sheet in Excel (and checked our work by filtering in the mother folder SQL) and found 809 companies that lacked timing info and the file predicteddidn't receive VC.txtThis file combined the predictions From this, we found 74 accelerators which we needed to the actual url of the websitescrawl for.
Since MTurk makes it hard for us to display We used the downloaded HTML, it is much faster crawler to just copy the url into the question box rather than trying to display the downloaded HTMLsearch for cohort companies listed for these accelerators.
The advantage to this is that some websites, such as techcrunch.com behaves abnormally when downloaded as HTML so opening these kinds of websites in During the browser would actually be more beneficial because the UI would not be messed up. Moreover, if certain websites has paywall or pop-up adsinitial test run, the user can also click out number of itgood pages was 359. 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 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 belowThe data is then handled by hand by fellow interns.
However. there The file for hand-coding is a disadvantage to thisin: 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 the other hand, downloaded HTMLs remain the same because it does not require any internet connection to render and thus, the content is static. /bulk/McNair/Projects/Accelerator Demo Day/Test Run/CrawledDemoDayHTMLFull/'''FinalResultWithURL'''
To create For the MTurk for this projectsake of collaboration, follow the team copied this tutorial in [[Mechanical Turk (Tool)]]. For testing and development purposeinformation to a Google Sheet, use accessible here: https://requestersandboxdocs.mturkgoogle.com/spreadsheets/d/16Suyp364lMkmUuUmK2dy_9MeSoS1X4DfFl3dYYDGPT4/edit?usp=sharing
Test account:email: mcboatfaceboaty670@gmailWe split the process into four parts.compasswordEach interns will do the following: sameastheoneforemail2018
For this project, all 1. Go to the fields that was asked of the user is:given URL.
*Whether 2. Record whether the page had a list of companies going through an accelerator*The month and year of the demo day is good data (or articlecolumn F)*Accelerator name*Companies going through accelerator; this can later be used by [[Minh Le]] to refine/fine-tune training data.
Layout: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).
[[File:Screen Shot 2018-07-25 at 114.37Record date, month, year, and the companies listed for that given accelerator.02 AM.png]]
==Hand Collecting Data==
During the initially test run, the number of good pages was 359. The data is then handled by hand by fellow interns.
===The Crawler Functionality===
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==
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.
==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 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.
