Text Data Mining
- Text data mining is described as the process extracting essential data from standard language text.
- Text mining is primarily used to draw useful insights or patterns from such data.
- Text mining is higher competition in the business market, many organizations seeking value-added solutions to compete with other organizations.
- With the increasing completion business and changing customer perspectives, organizations are making huge investments find a solution that is capable of analyzing customer and competitor data to improve competitiveness.
- The primary source of data is social media platforms, published articles, e-commerce websites, survey, and many more. Larger part of generated data is unstructured, makes it challenging and expensive for the organizations to analyze with help of the people.
Areas of Text Mining
- The following area of text mining :
Areas of Text mining
- Automatic Extraction of structured data such entities, entities relationships, and attributes describing entities from an unstructured source is called information extraction.
Natural Language Processing
- NLP is primarily a component of AI. The development of the NLP application is difficult because computers generally expect humans to "Speak" to them in a programming language that is accurate, clear, and exceptionally structured.
- Human speech is usually not authentic so that it can depend on many complex variables, including social context, slang, and regional dialects.
- Data mining tools can be used to resolve many business problems traditionally been too time-consuming.
- It refers to the extraction of useful data, hidden patterns from large data sets.
- It deals with retrieving useful data from data that is stored in our systems.
Text Mining Process
Process of Text Mining
- A technique that is used to control the capitalization of the text.
- Two way of document representation is given.
- Bag of words
- Vector Space
- A significant task and a critical step in Text Mining (Data pre-processing is used for extracting useful information and knowledge from unstructured text data), Natural Language Processing (NLP), and Information retrieval(IR) (Choosing which documents in a collection should be retrieved to fulfill the user's need.).
- Feature selection can be defined as the process of reducing the input of processing or finding the essential information sources. Feature selection is a significant part of data mining.
- Data Mining procedures are used in the structural database.
- It evaluates the results. Once the result is evaluated, the result abandon.
- There are the following text mining applications:
- Risk Management
- Customer Care Service
- Business Intelligence
- Social Media Analysis
- Risk Management Software based on text mining technology can effectively enhance the ability to diminish risk. It enables the administration millions of sources and petabytes of text documents, and giving the ability to connect the data. It helps to access the appropriate data at the right time.
Customer Care Service
- The objective of text analysis is to reduce the response time of the organizations and help to address the complaints of the customer rapidly and productively.
- Business firms and companies have started to use text mining strategies as a major aspect of their business intelligence. In addition, it providing significant insights into customer behavior and trends, text mining strategies also support organizations to analyze the qualities and weaknesses of their opponent's so, giving them a competitive advantage in market.
Social Media Analysis
- A social media platform that enables you to understand the response of the individuals who are interacting with your brand and content.
Text Mining Approaches in Data Mining
- Text mining approaches that are used in data mining.
- Keyword-based Association Analysis
- Document Classification Analysis
Keyword-based Association Analysis
- sets of keywords or terms that often happen together and afterward discover the association relationship among them. It preprocesses the text data by parsing, stemming, removing stop words, etc. Once it pre-processed the data, then it induces association mining algorithms.
Document Classification Analysis:
- Automatic document classification : It is used for the automatic classification of the large number of online text documents like emails, webpages etc. Text document classification varies with the classification of relational data as document databases are not organized according to attribute values pairs.
- A significant pre-processing step before ordering of input documents starts with the stemming of words. The primary purpose of stemming is to ensure a similar word by text mining program.
Support for different languages
- Some highly language-dependent operations such as stemming, synonyms, the letters that are allowed in words.
Exclude Certain Character
- Excluding numbers, specific characters, or series of characters, or words that are shorter or longer than a specific number of letters.
Include lists, exclude lists (stop-words)
- A particular list of words to be listed can be characterized, and it is useful when we want to search for a specific word. It also classifies the input documents based on the frequencies with which those words occur. Additionally, "stop words," which means terms that are to be rejected from the ordering can be characterized.
- Normally, a default list of English stop words incorporates "the," "a," "since," etc. words are used in respective language very often but communicate very little data in the document.