Of course, researching LinkedIn profiles for a huge list of people is a very repetitive task. So let’s find a solution to improve this workflow…
1. Python and TabPy
We use Python to build API requests, communicate with Azure Cognitive Services and to verify the returned search results. In order to use Python within Tableau, we need to setup TabPy. If you haven’t done this yet: checkout my TabPy tutorial.
2. Microsoft Azure Cognitive Services
One of many APIs provided by Azure Cognitive Services is the Web Search API. We use this API to search for name + company + “linkedin”. The first three results are then validated by our Python script. One of the results should contain the corresponding LinkedIn profile.
3. Calculated Field in Tableau
Let’s wrap our Python script together and create a Calculated Field in Tableau:
Anyone can analyze basic social media data in a few steps. But once you’ve started diving into social analytics, how do you bring it to the next level? This session will cover strategies for scaling a social data program. You’ll learn skills such as how to directly connect to your social media data with a Web Data Connector, considerations for building scalable data sources, and tips for using metadata and calculations for more sophisticated analysis.
Here are some key takeaways and links (i.e. additional resources) featured during my TC18 sessions to help you formulate your social media data program in order to build a stronger presence and retrieve powerful insights:
Step 1: Understand How to Succeed with Social Media
Apple has officially joined Instagram on 7th August 2017. This isn’t your average corporate account as the company doesn’t want to showcase its own products. Instead, Apple is going to share photos shot with an iPhone:
And there are plenty takeaways for every business:
Wrap your data around your customers, in order to create business value
Interact with your customer in a natural way
Understand your customer and customer behaviour better by analyzing social media data
Step 2: Define Your Social Objectives and KPIs
A previous record-holding tweet: In 2014, actor and talk show host Ellen DeGeneres took a selfie with a gaggle of celebrities while hosting the Oscars. That photo has 3.44 million retweets at the time of writing:
Are you ready for Tableau Conference 2018? Don’t miss my Social Media Analytics sessions!
Why do we need Social Media Analytics?
Social Media Analytics transforms raw data from social media platforms into insight, which in turn leads to new business value.
What will your learn in this sessions?
Once you dive into Social Media Analytics, how do you bring it to the next level? Social data can offer powerful insights right away. In this session, you will learn how to build a mature social data program from that foundation and strategies for scaling a social data programme, as well as how to connect directly to your social media data with a web data connector; considerations for building scalable data sources; and tips for using metadata and calculations for more sophisticated analysis.
Where and when are the sessions?
Do you want to learn more about Social Media Analytics with Tableau? Meet me at the 2018 Tableau Conferences in London or New Orleans and join my sessions:
With over 3 billion active social media users, establishing an active presence on social media networks is becoming increasingly essential in getting your business front of your ideal audience. These days, more and more consumers are looking to engage, connect and communicate with their favorite brands on social media.
Adding social media to your customer-centric data strategy will help boost brand awareness, increase followership, drive traffic to your website and generate leads for your sales funnel. In 2017, no organization should be without a plan that actively places their brand on social media, and analyzes their social media data.
Once you’ve started diving into social media analytics, how do you bring it to the next level? This session covers a customer-centric data strategy for scaling a social media data program.
Here are the links (i.e. additional resources) featured during the session to help you formulate your social media data program in order to build a stronger presence and retrieve powerful insights:
Until now the sentiment package for R only worked with English text. Today, I released version 1.0 of the sentiment package that features multi-language support. In order to perform sentiment analysis with German text, just add the parameter language="german" as shown in this example:
The new code allows you to add any language. So far, I started to prepare German sentiment files. French and Spanish are coming…