A Survey Of Data Mining Techniques For Social Media Analysis

What Is Data Mining?

Data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers to develop more effective marketing strategies, increase sales and decrease costs.

WHAT IS DATA MINING?

Data mining is a process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems

KEY POINTS

  • Data mining is the process of analyzing a large batch of information to discern trends and patterns.
  • Data mining can be used by corporations for everything from learning about what customers are interested in or want to buy to fraud detection and spam filtering.
  • Data mining programs break down patterns and connections in data based on what information users request or provide.
  • Social media companies use data mining techniques to commodify their users in order to generate profit.
  • This use of data mining has come under criticism lately s users are often unaware of the data mining happening with their personal information, especially when it is used to influence preferences.

How Data Mining Works.

Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. … Then, application software sorts the data based on the user’s results, and finally, the end-user presents the data in an easy-to-share format, such as a graph or table.

Most Useful Data Mining Applications of 2021

Data Mining Applications

1. Financial Analysis

The banking and finance industry relies on high-quality, reliable data. In loan markets, financial and user data can be used for a variety of purposes, like predicting loan payments and determining credit ratings. And data mining methods make such tasks more manageable. 

Classification techniques facilitate the separation of crucial factors that influence customers’ banking decisions from the irrelevant ones. Further, multidimensional clustering techniques allow the identification of customers with similar loan payment behaviours. Data analysis and mining can also help detect money laundering and other financial crimes.

2. Telecommunication Industry

Expanding and growing at a fast pace, especially with the advent of the internet. Data mining can enable key industry players to improve their service quality to stay ahead in the game. 

Pattern analysis of spatiotemporal databases can play a huge role in mobile telecommunication, mobile computing, and also web and information services. And techniques like outlier analysis can detect fraudulent users. Also, OLAP and visualization tools can help compare information, such as user group behaviour, profit, data traffic, system overloads, etc. 

3. Intrusion Detection

Global connectivity in today’s technology-driven economy has presented security challenges for network administration. Network resources can face threats and actions that intrude on their confidentiality or integrity. Therefore, detection of intrusion has emerged as a crucial data mining practice.

It encompasses association and correlation analysis, aggregation techniques, visualization, and query tools, which can effectively detect any anomalies or deviations from normal behaviour. 

4. Retail Industry

The organized retail sector holds sizable quantities of data points covering sales, purchasing history, delivery of goods, consumption, and customer service. The databases have become even larger with the arrival of e-commerce marketplaces. 

In modern-day retail, data warehouses are being designed and constructed to get the full benefits of data mining. Multidimensional data analysis helps deal with data related to different types of customers, products, regions, and time zones. Online retailers can also recommend products to drive more sales revenue and analyze the effectiveness of their promotional campaigns. So, from noticing buying patterns to improving customer service and satisfaction, data mining opens many doors in this sector. 

5. Higher Education

As the demand for higher education goes up worldwide, institutions are looking for innovative solutions to cater to the rising needs. Institutions can use data mining to predict which students would enrol in a particular program, who would require additional assistance to graduate, refining enrollment management overall.

Moreover, the prognosis of students’ career paths and presentation of data would become more comfortable with effective analytics. In this manner, data mining techniques can help uncover the hidden patterns in massive databases in the field of higher education.

6. Energy Industry

Big Data is available even in the energy sector nowadays, which points to the need for appropriate data mining techniques. Decision tree models and support vector machine learning are among the most popular approaches in the industry, providing feasible solutions for decision-making and management. Additionally, data mining can also achieve productive gains by predicting power outputs and the clearing price of electricity.  

7. Spatial Data Mining

Geographic Information Systems (GIS) and several other navigation applications make use of data mining to secure vital information and understand its implications. This new trend includes extraction of geographical, environment, and astronomical data, including images from outer space. Typically, spatial data mining can reveal aspects like topology and distance. 

 8. Biological Data Analysis

Biological data mining practices are common in genomics, proteomics, and biomedical research. From characterizing patients’ behaviour and predicting office visits to identifying medical therapies for their illnesses, data science techniques provide multiple advantages. 

Some of the data mining applications in the Bioinformatics field are:

  • Semantic integration of heterogeneous and distributed databases
  • Association and path analysis
  • Use of visualization tools
  • Structural pattern discovery
  • Analysis of genetic networks and protein pathways

9. Other Scientific Applications

Fast numerical simulations in scientific fields like chemical engineering, fluid dynamics, climate, and ecosystem modeling generate vast datasets. Data mining brings capabilities like data warehouses, data preprocessing, visualization, graph-based mining, etc. 

10. Manufacturing Engineering

System-level designing makes use of data mining to extract relationships between portfolios and product architectures. Moreover, the methods also come in handy for predicting product costs and span time for development. 

11. Criminal Investigation

Data mining activities are also used in Criminology, which is a study of crime characteristics. First, text-based crime reports need to be converted into word processing files. Then, the identification and crime-machining process would take place by discovering patterns in massive stores of data. 

12. Counter-Terrorism

Sophisticated mathematical algorithms can indicate which intelligence unit should play the headliner in counter-terrorism activities. Data mining can even help with police administration tasks, like determining where to deploy the workforce and denoting the searches at border crossings. 

Choosing a data mining system

Data mining lies at the junction of machine learning, statistics, and database systems. As we discussed earlier, it can empower modern-day industries in diverse ways. The selection of a suitable data mining system generally depends on the following factors.

  • Type of Data: Before choosing a mining system, we need to check the format of data that its existing infrastructure can handle. The data can be record-based, relational, or in the form of ASCII text, database or warehouse data, etc. 
  • Type of Sources: Data sources surface as another consideration while selecting a data mining system. Some data mining systems work on relational sources, while others may operate only on ASCII text files. Ideally, the system should also support features like Open Database Connectivity. 
  • System issues: The data mining system should be compatible with one or more operating systems. Certain structures also provide web-based UIs and allow XML data inputs. 
  • Data mining methodologies: Choose your data mining system based on the functions offered. While some units may be equipped with only one methodology, say classification, others may provide multiple capabilities. Examples include concept description, association mining, clustering, prediction, discovery-driven OLAP analysis, linkage analysis, similarity search, outlier analysis, etc. 
  • Database or data warehouse systems: You would have to couple your data mining system with a database or a data warehouse to create an integrated and uniform environment fit for information processing. There are different types of coupling available, such as No Coupling, Loose Coupling, Semi tight Coupling, and Tight Coupling.
  • Scalability: Scalability of database size (row) and dimension (column) emerges as yet another significant aspect of a data mining system. When the number of rows goes up by ten times, and the system takes no more than ten times to execute a query, it is considered row scalable. On the other hand, a mining system can be assessed as column scalable if there is a linear increase in the query execution time as more columns are added.
  • Visualization tools: The choice of a data mining system would also take its visualization competencies into account. The capacities can range from data visualization to the mining process and result visualization. 
  • User interface: A user-friendly graphical interface is essential for interactive data mining. While relational database systems may require the use of query languages, the same does not hold for data mining systems.

Data Mining Techniques

  • Scalable and interactive data mining methods: Added controls in the form of specifications and constraints can guide data mining systems in not only effectively handling huge volumes of data but also searching for interesting patterns. 
  • Standardization of query languages: Standard querying languages will improve interoperability between different data mining functions and promote systematic development of solutions. 
  • Visual data mining: Visual data mining has picked up pace as one of the top data mining trends, presenting innovative opportunities for knowledge discovery. 
  • Research analysis: Data mining applications are not limited to the tech world. Data cleaning, preprocessing, visualization, and integration of databases have transformed the broad field of research. 
  • Web mining: Web content mining, web log mining, and other mining services on the internet have secured a place among the flourishing subfields of data mining. 
  • Multi-database and distributed data mining: Multidatabase data mining analyzes patterns across multiple databases. Whereas distributed data mining searches data from several network locations. 
  • Real-time data mining: Real-time data or ‘stream data’ is generated from web mining, mobile data mining, e-commerce, stock analysis, etc. This type of data requires dynamic data mining models. 
  • Privacy protection and information security have also come to light as a notable trend in the data mining space.

What is the most common application of data mining?

The importance of data mining is consistently growing in every sector around the world. Big Data is analyzed by every organization with the use of data mining and its tools. No matter what sector the company is in, they need to analyze and understand the data for coming up with actionable insights. This process will completely impact the sales of that particular company with time. Some of the most common applications of data mining are in the below sectors
1. Mobile Service Providers.
2. Crime Prevention.
3. Farming.
4. Insurance
5. Transportation.
6. Retail Sector.
7. Research.
8. Science and Engineering.
9. Artificial Intelligence.

Data Mining has turned out to be an essential part of every organization in every sector. If you wish to understand your customer’s behavior, then you need to focus on data mining.

How do banks use data mining?

With the increase in the number of channels (mobile banking, retail banking, ATM, internet banking, etc.), banks have a huge amount of data collection. In order to help the banks in making better decisions, it is necessary to extract meaningful information from these loads of data available.
For instance, classification is the most common as well as the most popular method being used in data mining. This can help a lot to churn out data, and it could be utilized for estimating bank customer churns, detecting frauds, evaluating loan approvals, and even predicting bank failures.
Other than that, data mining is also useful in various other sectors of banking such as customer segmentation and profitability, cash management, forecasting operations, optimizing stock portfolios, credit score and approval, and also marketing.

Why is data mining so popular?

Right now, data is of the essence for every industry. A company with a huge amount of data can easily convert the data into customers with the help of the right knowledge, tools, and methods. By applying different patterns on junk datasets, one can obtain valuable insights and also understand upcoming market trends. Every company wants to gain more customers and also retain the existing ones. This is made possible with the help of insights generated through the extracted data. This is the main reason behind the immense popularity of data mining among companies.

A Survey of Data Mining Techniques for Social Media Analysis

WHAT IS SOCIAL MEDIA?

Social media

Social media are interactive technologies that allow the creation or sharing/exchange of information, ideas, interests, and other forms of expression via virtual communities and networks

What Is Social Media?

The term social media refers to a computer-based technology that facilitates the sharing of ideas, thoughts, and information through virtual networks and communities. Social media is internet-based and gives users quick electronic communication of content, such as personal information, documents, videos, and photos. Users engage with social media via a computer, tablet, or smartphone via web-based software or applications. While social media is ubiquitous in America and Europe, Asian countries like Indonesia lead the list of social media usage.1 More than 3.8 billion people use social media.

KEY POINTS

  • Social media is a computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities.
  • There are more than 3.8 billion social media users around the world.
  • The largest social media networks include Facebook, Instagram, Twitter, YouTube, and TikTok.
  • Social media typically features user-generated content and personalized profiles.
  • By 2023, the number of social media users in the United States is forecast to increase to approximately 257 million.

Understanding Social Media

Social media originated as a way to interact with friends and family but was later adopted by businesses that wanted to take advantage of a popular new communication method to reach out to customers. The power of social media is the ability to connect and share information with anyone on Earth, or with many people simultaneousl

There are more than 3.8 billion social media users around the world. Social media is an ever-changing and ever-evolving field, with new apps such as TikTok and Clubhouse coming out seemingly every year, joining the ranks of established social networks like Facebook, YouTube, Twitter, and Instagram. By 2023, the number of social media users in the United States is forecast to increase to approximately 257 million.

According to the Pew Research Center, social media users tend to be younger. Nearly 90% of people between the ages of 18 and 29 used at least one form of social media. Further, these users tend to be better educated and relatively wealthy, or earning over $75,000 per year.4

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis

How does social media data mining work?

Social data first needs to be collected and processed. This is data that is publicly available, which may include age, sex, race, geographic location, job profession, schools you’ve attended, languages you speak, friends and connections, networks you belong to, and more.

Then there’s the unstructured content of what you post on social media – like tweets, comments, status updates – which is mainly what businesses, firms, and agencies are looking to mine. So, if your profiles are completely public, just understand this is generally fair game for social media data mining.

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