I was asked to find Twitter accounts associated with the Dominican Republic (the project had to do with voting). This was a strange request since some twitter accounts have GeoSpatial data associated with the account, we have no idea whether it is accurate.
I wound up searching by hand for keywords that I knew were related: #dominican, #washingtonheights and I hopped along their friends and followers and I found the people I was looking for.
More genereally:
How do I search for Twitter accounts associated with a given topic? How might it be possible to train a bot to identify hashtags relevant to a given topic? And then we can search for those keywords.
#Moderators: This is not really a coding question. If you can think of a better StackExchange, please migrate this!
Since you already have a given Topic i would suggest he following:
Get a couple of Account by Hand by these Hashtags you already mentioned.
Retrieve X tweets for these Accounts
Do some Natural Language Processing of these Tweets to get new ideas for Keywords.
Some things i used in this/similar contex:
tf-idf + NMF to get Topics and then sort by components to retrieve
the topics a user is talking about (user can have multiple topics).
some sort of clustering (your biggest problem here will be the high
sparesity of the data, so PCA could be an option)
use wordnet etc to collect similar keywords
Related
I'm helping develop an online ordering process for a local business, and they are offering food delivery to their location from specific partnered restaurants. The problem we're looking to solve is making the experience as smooth as possible for the user, so we're aiming to fill out these specific data fields to make the process smoother. They have QR codes on their tables, that link directly to their websites ordering page, from there the user can select from a few local restaurants, and are linked to their online ChowNow menus.
Does anyone have any experience using ChowNow, and knows if there is any way to pass off specific delivery instructions to ChowNow, specifically an address, from the link provided to the users from the QR code on their table?
We haven't found anything too helpful on this problem from ChowNow's official documentation or support. There are alternate ways that we have in mind to solve this problem if need be, but this is the desired solution from the client, and we would like to have stick with this method if possible.
I am building a machine learning model that would suggest attractions in a specific location.
I have most of the details worked out. However, I still need to collect the data of the attractions to train my model.
Is there somewhere I could find a dataset for this (I already checked Kaggle)? If not which websites should I scrape?
If you want to scrape data, twitter probably is the easiest to start. You can use twitter API to get any tweet that contain a specific keyword or hashtag, input your desired location as the keyword and scrape it using tweepy, i would suggest you to scrape from a specific account like Influencer or travel blog to get data about attraction.
Applying to get twitter API might take several days, and you can only scrape tweet within a time range of a weeks. older than that you need to sign up to their premium subscription.
Having a history of the subscriber count of specific subreddit is data that I need for data analysis.
I found a page which is doing exactly that e.g.: http://redditmetrics.com/r/worldnews
Is there an official endpoint in the reddit api for getting a history of subscriber counts and how the popularity of a subreddit has evolved until now or are there any other reliable ways to do this?
Tracking the data on my own is not an option because I'm interested in subscriber counts in the past and not in the future past.
Unfortunately there's no endpoint for that within the reddit api. One option could be to scrape sites like that for past data (with permission hopefully). And maybe the only other option would be to ask those sites for raw data (who knows, it might work). Good luck!
I have list of users
['foo','bar']
I want to search whether they have checked in somewhere or not (using 4sq api)..
So basically, all I am looking for is that whether their tweets contain "\4sq.com\" or not?
I get very confused looking into their api?
Bonus points if the steps can be implemented in python.
Thanks
You have two options for checking a user's tweets:
One option is to look through the tweets in their User Timeline (accessible in Tweepy through api.user_timeline). However, you may have to search through a lot of unrelated Tweets before you come across the one your looking for. Given how many tweets some users have, you might want to only look thorough tweets more recent than a certain date (you can look at the created_at attribute of returned tweets).
The other option is to use the Search API (accessible in Tweepy though 'api.search'). This has the advantage of allowing you specify a search query, giving you relevant tweets. However, you will need to search through the tweets until you find one by the user you're looking for. Again, you might want to limit the date range of tweets that you search through.
I enter this reference into the google search field
Nature 2008 May 8;453(7192):164-6
I expect at least one link to be from the nature.com website, I mean it says "Nature" in the query. All results are from ncbi, which only collects abstracts. Is that so hard? Usually references in journals are in such or a similar format... How come it's not recognized as such?
Please redirect the question to the appropriate stackexchange sub-field if necessary.
When Google lists query results, it takes into consideration website popularity. Your paper is both on ncbi and nature.com and because nsbi website is ranked at 361-st and nature.com is at 2,984-th for internet traffic from alexa.com, your Google results will be like that.