Crowdsourcing Workshift > Find work > Social media data cleaning
Job name
Social media data cleaningRequirements
1. Area
United States
2. Need Identity Verification
No
3. Skills
Description
Object: Identify and extract commercial comments in tweet posts.
Requirements:
The task does not require any specific analytical skills, but requires basic Excel skills.
The task requires you to read through the tweets posted by athletes and take memos when you find corporate/ product names in their tweet texts.
Data volume: 119,324 tweets
Method: See the example sheet attached.
Identify commercial comments (including corporate name, product, champaign, university name) in the list of tweet posts (text colum) and extract commercial elements in the right columns.
Categories of the commercial elements:
I. Corporate
Example post: " Just joined the @Killerspin family! So excited :D"
Commercial element: "Killerspin" (table tennis equipment company)
Operation: Put "Killerspin" in the "corporate" column
II.product
Example post: "RT @TheSliceTweets: Hanging out at Duane Reade with @Bryanbros @Bryanbrothers and the @NestleNesquik Bunny! http://t.co/DkhUtrk0"
Commercial element: "NestleNesquik"
Operation: Put "Nesquik" in the "product" column
III.university(affiliation)
Example post: "A pic with the @Reds Rosie Red. She is now my 2nd favorite mascot behind of course Stanford's The Tree. http://t.co/Wynld4u8"
Commercial element: "Stanford"
Operation: Put "Stanford" in the "university" column
IV.champaign
Example post: "Yesterday we stopped by the USA House to see the @CitiEveryStep Footprint Wall. What a honor to be on there. http://t.co/oTetsolC"
Commercial element: "CitiEveryStep"
Operation: Put "CitiEveryStep" in the "campaign" column
V. Other
Other posts including commercial intention
*note: if there are several commercial elements in a post, extract all elements and put them into appropriate columns
Example post: "Nestle If all goes wrong at the Open at least we'll leave as the Nesquik ping pong champs. http://t.co/kMLQpTS0"
Operation: "Nestle" to "corporate" column, "Nesquik" to "product" column
if there are several commercial elements but in the same category in a post, extract all elements and put them into same category colums
Example post: "RT @asiance: @topspinmovie @ArielHsing hahah yeah! We're working on them!! Killerspin"
Operation: corporate:"asiance"(new line)"Killerspin" to the "corporate" column
I will send you the data set once we agree on the contract.
Introduction by Client and Background of Job
About Fee
-
Project Budget (tax included)
Client Desired Fee 100,000 yen to 200,000 yen -
Details of remuneration
About 1 cents per tweet.
Usually those commercial tweets appear only 5 out of 300 tweets. You can just read through uncommercial tweets without any tasks. We would like to propose 120,000 yen in total.
Posting Ends
Desired Delivery Date
Attachment
Delivery Format
Criteria for Hiring
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Client information
Proposal Date: 2018-09-08 03:34 | |
Proposal Date: 2018-09-07 16:36 | |
Proposal Date: 2018-09-07 16:07 | |
Proposal Date: 2018-09-06 17:48 | |
Proposal Date: 2018-09-06 01:19 | |
Proposal Date: 2018-09-05 15:00 | |
Proposal Date: 2018-09-05 12:24 | |
Proposal Date: 2018-09-04 23:01 | |
Proposal Date: 2018-09-04 20:51 | |
Proposal Date: 2018-09-04 16:53 | |
Proposal Date: 2018-09-04 15:09 |