From the course: Data Science and Analytics Career Paths and Certifications: First Steps

Social media analytics

From the course: Data Science and Analytics Career Paths and Certifications: First Steps

Start my 1-month free trial Buy for my team

Social media analytics

“

- [Narrator] More and more people are using social media. This in turn, generates an enormous amount of data. Data scientists are naturally attracted to this newly emerging type of datasets. Social media refers to websites where users can post their own content to share it with their friends and beyond. Depending on their focus, social media sites have different types of interests they promote. For example, Facebook offers a forum for building informal and personal relationships compared to a professional networking tool like LinkedIn. In addition to its size, qualifying as big data another unique value of social media data lies in the data about data or metadata it carries. For example, a post on Facebook can accompany location information as well as timestamps. With this kind of unstructured but very rich data sets, a lot of useful insight can be derived about a person who is posting and consuming information. Social media analyzer products are emerging and offering profiling services for companies that want to know more about their customers or future employees. In the case of social media analytics, text mining and parsing are the crucial and necessary first steps. Social media companies often make their content available through their application programming interface or API. Using this API, data scientists can retrieve the data they want. Collecting the social media data is one thing but manipulating it for analysis purposes is another. A lot of skills and efforts are necessary before attempting to apply analytics methods although standards like JSON helps.

Contents