Exploring social data applications

Now that you know where the future of the web is heading, let's shift our focus back to our discussion on the purpose of analyzing the social web data. We have discussed about the nature of social media and the social data, structured and unstructured, but you must be curious as to how this is used in the real world. In our view, restricting the application of social data analytics to certain fields or sectors is not entirely fair. Social data analytics leads you to the learning or discovery of facts or knowledge. If acquiring knowledge can't be restricted to a few fields, neither can be social media analytics. However, there are some fields that are prospering more from this science, such as marketing, advertising, and research communities. Social media data is being integrated more and more in existing digital services to provide a much more personalized experience through recommendations. You must have seen that most online services allow you to register using your social profiles along with added information. When you do so, the service is able to mine your social data and recommend products or catalogs aligned with your interests. Entertainment services like Spotify and Netflix, or e-commerce ones like Amazon, eBay, and others, are able to propose personalized recommendations based on social data analytics and other data sources. More traditional companies selling consumer products derive value from social data in their marketing of products and brands. People use social networks as a means to both connect with companies and to express about their products and services. Hence, there is a huge amount of data on the social web that contains customer preferences and complaints about companies. This is an example of unstructured-social data, since it's mostly textual or images in format. Companies are analyzing this data to understand how consumers feel and use their services or campaigns, and then are using this intelligence to integrate it in their marketing and communications.

A similar approach has been applied in political campaigns to understand the opinion of people on various political issues. Analysts and data scientists have gone as far as trying to predict election results using sentiments of people about the concerned politicians. There are certainly many data scientists using social media data to predict the results of Clinton and Trump elections. There have been attempts to predict the stock market using social data but this has not been very successful, as financial data is highly sensitive and volatile and so is social data, and combining the two is still a big challenge.

In the later chapters, you'll see how we can analyze the Facebook page of a brand to understand their relation with their consumers. In Chapter 7, Scraping and Extracting Conversational Topics on Internet Forums about analyzing forums, you'll see how we are able to understand deeper conversations regarding certain subjects. Building recommendation engines is beyond the scope of the book, but you'll know enough about social data in order to integrate it for your recommender system projects.

Now that you know enough about social media, social data, and their applications, we will dive into the methods to get on top of social data. Among the many techniques used to analyze social data, machine learning is one of the most effective ones.