Introduction to Text Mining
The mining process of text analytics to derive high-quality information from text is called text mining. The information is collected by forming patterns or trends from statistic methods. Due to this mining process, users can save costs for operations and recognize the data mysteries. The unstructured data is converted into useful information with the help of NLP or any other AI technologies. It deals only with the text and the patterns of text. It can be used in customer care service, cybercrime prevention and detection, and business intelligence.
What is Text Mining?
- This process has become more practical because of the big data. The data scientists and other users use big data and deep learning to analyze massive sets of unstructured data.
- After identifying the facts, relationships, and also assertions, all these facts are extracted and analysed, to analyze first turned into structured data, visualization with the help of HTML tables, mind maps, charts, etc., integration with structured data in databases or warehouses, and further classify using machine learning (ML) systems.
- The sources of mining and analyzing could be corporate documents, customer emails, survey comments, call centre logs, social network posts, medical records, and other text-based data sources, helping a business find potentially valuable business insights.
- Text Mining and Natural Language Processing (NLP) are Artificial Intelligence (AI) technologies that allow users to rapidly transform the key content in text documents into quantitative, actionable insights.
How does Text Mining make Working so Easy?
- It works the same as data mining but focusing on text instead of more structured forms of data. The first step in this process is to organize the data in terms of quantitative and qualitative analysis to use natural language processing (NLP) technology.
- Its work includes information retrieval or identification (collect the data from all the sources for analysis), apply text analytics (statistical methods or natural language processing to part of speech tagging), named entity recognition (identify quoted text features the process name as categorizing), disambiguation (clustering), document clustering ( to identify sets of similar text documents), place the noun and other terms that refer to the same object, then find the relationship and fact among entities and additional information in text, then perform sentiment analysis and quantitative text analysis and then create the analytic model that helps to generate business strategies and operational actions.
What can you do with Text Mining?
- The best example of text mining is sentiment analysis that can track customer review or sentiment about a restaurant, company and so on also known as opinion mining, in this sentiment analysis collects text from online reviews or social networks and other data sources and perform the NLP to identify positive or negative feelings of customers. Theses information is further used to solve the negative point, improve customer satisfaction, and help in marketing and other areas of improvement.
- Other common uses include Security applications, Biomedical applications for clinical studies and precision medicine analyzing descriptions of medical symptoms to aid in diagnoses, marketing like analytical customer relationship management, add targeting, screening job candidates based on the wording in their resumes, Scientific literature mining for a publisher to search the data on index retrieval, blocking spam emails, classifying website content, identifying insurance claims that may be fraudulent, and examining corporate documents as part of electronic discovery processes.
Advantages
Given below are the advantages mentioned:
- It helps in fraud detection for the insurance company, risk management, scientific analysis, customer behaviour, and so on, which allows the company to improve.
- It helps companies detect issues and then resolve them before becoming a big problem that affects the company. In addition, the customer reviews and communications can improve the customer experience by identifying required features for customers and improvement by all, which increase the sale and then increase revenue and profit of the company.
- Even text mining in healthcare enables us to identify the disease and diagnose disease.
Required Skills
To perform text mining, people should have data analysis skills, be useful in statistics, Big data processing frameworks, Database knowledge, Machine Learning or Deep Learning algorithms, Natural Language Processing, and, apart from this, good in the programming language.
Scope
It is a fast-growing field as the big data field is growing, so the scope is very promising in the future as the amount of Text Data is increasing exponentially day by day. Moreover, social media platforms generate many text data that can be mined to get real insights into different domains.
Right Audience for Learning these Technologies
The target audience for learning this technology are professionals who want to identify the valuable insights the huge amount of unstructured data for the companies for different purposes like increase the sales and profits of the company, fraud detection for the insurance company as well in the field of health and even scientists to perform the scientific analysis and all.
Conclusion
Is also known as text data mining is the process of extracts and analyzes data from large amounts of unstructured text data. It work includes information retrieval or identification, apply text analytics, named entity recognition, disambiguation, document clustering, identify the noun and other terms that refer to the same object, then find the relationship and fact among entities and additional information in text, then perform sentiment analysis and quantitative text analysis and then create the analytic model that helps to generate business strategies and operational actions. It helps in fraud detection, risk management, scientific analysis, customer behaviour, healthcare, etc. To perform the mining, data analysis, statistics, big data processing frameworks, database knowledge, Machine Learning or Deep Learning Algorithm, Natural Language Processing, and apart from this good in the programming language. It is a fast-growing field as the big data field is growing, so the scope for this is very promising in the future.
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