A concept important to understand when handling unstructured data is semantics. Dictionaries define the term as the branch of linguistics and logic concerned with meaning.

It is a concept that comes from linguistic science and philosophy, to deal with the study and research of meaning. These meanings are uncovered by understanding the relationship between words, phrases, and all types of symbols. From a social media point of view, symbol could be the popular emoticons, which are not exactly formal language but they signify emotions. These symbols can be extended to images and videos, where patterns in their content can be used to extract meanings. In the later chapters, we will show few techniques that can help you to get meaning out of textual data. Extracting meaning or sense from images and videos is out of scope for the book. Semantic technology is very central to effectively analyzing unstructured social data.

For effectively extracting sense out of social data, semantic technologies have underlying artificial intelligence or machine learning algorithms. These algorithms allow you to find patterns in the data, which are then humanly interpreted. That's why social data analytics is so exciting, as it brings together knowledge from the fields of semantics and machine learning, and then binds it with sociology for business or other objectives.

The growth of the internet has given rise to platforms like websites, portals, search engines, social media, and so on. All of these have created a massive collection of content and documents. Google and other search engines have helped to organize these documents and make them accessible to everyday users. So, today we are able to search our questions and select websites or pages that are linked to the answer. Even social media content is more and more accessible via search engines. You may find a tweet that you created two years back suddenly showing on a Google result. The problem of organization of web content is almost a solved problem. However, wouldn't it be more exciting if you asked a question on Google, Bing, or another search engine and it directly gave you the answer, just like a friend with the required knowledge? This is exactly what the future web would look like and would do. Already, for example, if you put the query on Google about what's the temperature in Paris? or who is the wife of Barack Obama?, it gives you the right answer. The ability of Google to do this is inherently semantic technology with natural language processing and machine learning. Algorithms that Google has behind its search engine creates links between queries and content by understanding relation between words, phrases, and actual answers.

However, today only a fixed number of questions can be answered, as there is big risk of inferring wrong answers on multiple questions. The future of the internet will be an extension to the World Wide Web, which is the semantic web. The term was coined by the creator of the World Wide Web, Tim Berners-Lee. The semantic web is a complex concept on a simple idea of connecting entities (URLs, pages, and content) on the web through relations, but the underlying implementation is difficult at scale, due to the sheer volume of entities present on the internet. New markup languages called Resource Description Framework (RDF) and Web Ontology Language (OWL) will be used to create these links between pages and content, based on relations. These new languages will allow creators of content to add meaning to their documents, which machines could process for reasoning or inference purposes, allowing automating many tasks on the web. Our book is not about explaining the underlying concepts of the semantic web, but just for your knowledge about where the web is heading and to appreciate the needs to mine the web more intelligently, as you'll learn in the later chapters.

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.