- #Twitter insights install
- #Twitter insights upgrade
- #Twitter insights full
- #Twitter insights code
- #Twitter insights tv
Here’s a screen shot of each Audience insights category from the account: Overview It’s accessible for signed in users from (click on the “Analytics” menu item and select “Audience insights”) or (click on the “Followers” tab).
#Twitter insights full
The dashboard, which also enables comparisons of a marketer’s followers or organic audience to the full Twitter audience, is available to all Twitter advertisers and users of Twitter analytics. comedy), type of credit card, consumer good preferences (bottled water? coffee? Ice cream and novelties?). weight conscious), favored television genre (drama vs. Based on this information, you can identify the best segments to target within Twitter Ads, along with which creative - such as a Vine or video clip - your audience will find most compelling.Īmong the categories tracked in the insights dashboard: gender, occupation, household income and net worth, home type, home value, marital status, education, consumer buying styles (premium brand vs.
#Twitter insights tv
It should be especially useful for advertisers, as Twitter product manager Andrew Bragdon explained in a blog post:įor example, if you’re running a campaign to increase awareness about a new cosmetics line, you can use this tool to learn about your potential customers on Twitter - the beauty products they’ve recently purchased, what fashion trends they’re interested in and even TV viewing behavior. The enhanced analytics, Twitter said, will help brands improve their paid and organic strategies on Twitter. Twitter said it plans to roll it out more broadly in the coming months.
That information, aggregated to protect user privacy, is currently only available for U.S. Twitter matches its data with consumer information from its Marketing Platform Partners, including Datalogix.
#Twitter insights upgrade
You can apply additional search criteria to include restricting to a date range, number of tweets to return, etcĬheck out the other blog posts in this series of Twitter Analytics using Python.Twitter today launched an upgrade to its analytics tool, offering marketers more information about audiences on the social communications network.Ĭalled “Audience insights,” the new feature drills into demographics, interests, lifestyle, consumer purchasing behavior, television-viewing preferences and mobile carrier and device usage. The following is an example of searching for a hash tag.įor tweet in tweepy.Cursor(api.search,q="#machinelearning", This can be hash tags, particular phrases, users, etc. Tweepy comes with a Search function that allows you to specify some text you want to search for. The following will take the last 10 tweets.įor tweets in tweepy.Cursor(api.home_timeline).items(10):Īn alternative is, that returns only 20 records, where the example above can return X number of tweets. You can also start listing the last X number of tweets from your timeline. Print('Listed: ' + str(user.listed_count)) Print('Followers: ' + str(user.followers_count)) Print('Twitter Name: ' + user.screen_name) #Get twitter information about my twitter account The following is an example about my Twitter account. There is a API function called 'me' that gathers are the user object details from Twitter and from there you can print these out to screen or do some other things with them. The easiest way to start exploring twitter is to find out information about your own twitter account. After that you will need to use the important codes that were defined on the Twitter webpage produced in Step 1 above, to create an authorised connection to the Twitter API.Īfter you have filled in your consumer and access token values and run this code, you will not get any response.
The first thing you need to do is to import the tweepy library.
#Twitter insights code
You are all set to start writing Python code to access, process and analyse Tweets.
Step 3 - Initial Python code and connecting to Twitter
#Twitter insights install
It will download and install tweepy and any dependencies. Make sure to check out the Tweepy web site for full details of what it will allow you to do. There is the Tweepy library that is very popular. Step 2 - Install libraries for processing Twitter DataĪs with most languages there is a bunch of code and libraries available for you to use. The details contained on this web page (and below what is shown in the above image) will allow you to use the Twitter REST APIs to interact with the Twitter service.
Keep the information on this page safe as you will need it later when creating your connection to Twitter. You will then get a web page like the following that contains lots of very important information. Then click the 'Create your Twitter Application' button. Then give the Name of your app (Twitter Analytics using Python), a description, a webpage link (eg your blog or something else), click on the 'add a Callback URL' button and finally click the check box to agree with the Developer Agreement.
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