Web mining refers techniques used to scrape the Internet to uncover key data points that can be analyzed to provide intelligence relevant to the task at hand. Professionals who understand how data is sourced and how web sites are used by consumers are increasingly in demand as software engineers, content developers, business analysts, and digital marketing strategists. The three main types of web mining include:
Structure mining looks at the way the Internet is organized in terms of relationships and links between various pages and objects. It helps to uncover connections between data of different kinds via keyword associations and content similarities. An example of structure mining would be the analysis of a web site’s home page and all of the links that emanate from that page.
In the context of analytics, which continues to grow as a valuable field of study for digital industry professionals, structure mining can indicate relationships among web sites or among the pages of a single web site. This information is hugely useful in business as it shows a correlation between content, traffic and user actions, such as the finalization of an online sales transaction or exit from the site with items still in their cart.
Content mining takes structure mining to the next level by analyzing the content located on all of these interrelated pages. This includes text, photos, and other graphics. Content mining is the basis for all search engine activities.
Digital designers and developers look at content mining as the vehicle for delivering the right information to the right customer at the right time. The focus is on providing the most relevant content based on the customer’s search parameters. Structuring the search results in order of relevance will pull the most valuable data to the top, thus encouraging further exploration of the company web site.
Usage mining is the third type of web content mining and is concerned with how the content on the Internet is used from a transactional perspective. This type of web mining helps businesses analyze their site activity to understand, predict, and improve user behaviors through site modification or personalization functionality.
Analysis of tracking data will show when and where users drop out of the process. This information helps business analysts look for newer and better ways to keep visitors engaged. Usage mining can also show where users go once leaving the site. With this knowledge, web developers, analysts and marketers can work together to reassess structure and content mining to compare their web site to the sites that their most valuable visitors also frequent.
Web Mining Trends
Social Network Mining
The amount of information that is freely shared among social networks provides a bonanza of data for businesses and researchers studying user behaviors and needs. This type of web mining can provide information on social community members and their relationships, how members are connected across multiple networks, and how they are using networks in real time. All of this data has significance for companies seeking to identify social network trends to improve marketing efforts. Like all web mining, data collection and analysis is driven by complex algorithms.
Sentiment analysis is just what it sounds like. It is a type of web content mining that examines natural language to determine if feelings being expressed by user are positive, negative or neutral. Sentiment analysis is essential to reputation management on the web. There are dozens of software programs available to help businesses evaluate customer, employee and vendor sentiments; identify the most popular topics and concepts; and monitor changes in sentiment over time. In a time when brands and personas can be ruined overnight, professionals with reputation management skills are in great demand at organizations around the world.
Web semantics go deeper than structure mining, down to the framework level. Web semantics includes the study of ontology, human-computer interaction, database construction, information retrieval, search agents, web standards and more. Industry experts believe that there is significant untapped potential in web semantics in terms of the original vision of technologists. Today’s tools are not yet robust enough to achieve most goals, primarily because of the amount of data available online. Software engineers who have the knowledge to advance “SemWeb” processing and analytics will find themselves on the leading edge of innovation in today’s IT world.