I want to share with you an overview of the steps a user takes to build a project in Watson analytics for Social media and why each step makes social data more relevant, trustworthy, and valuable.
Bring Watson analytics for Social media the questions that make you scared, excited, confused, or optimistic. Use it to relieve or confirm your fears about disruption in your industry. Use it to understand reputational risk, loyalty among your own customers, and those of your competitors. Use it to find answers to questions you didn’t even know to ask.
Social media is vast – there are thousands of thousands of posts added to conversations across social media sites each day.
Some of it is relevant to you, and some of it is not. What’s relevant really depends on the question you need to answer. To trim social media conversations down to just the relevant ones, create topics. Each topic (you need one, but you could create dozens) is a list of keywords that must appear in a post in social media for that post to be added to your dataset. Basically, you’re creating a custom subset of social data that contains only relevant content.
You should create a list of include terms for each topic because there are often many ways to refer to the same topic – tennis shoes and sneakers and trainers, for example. You aren’t on your own to figure this out, you have Watson’s cognitive power to help you. The feature is called Topic Suggestions, and it pulls a real sample of social data based on what you’ve added to your topic so far. If your Topic Suggestions are relevant, you can be sure that your final dataset will include only relevant data.
Themes are the ways in which your custom subset of social data will be classified – this is where text analytics comes in.
