{"id":9489,"date":"2018-02-27T13:28:05","date_gmt":"2018-02-27T13:28:05","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=9489"},"modified":"2021-05-28T13:01:53","modified_gmt":"2021-05-28T07:31:53","slug":"text-mining","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/text-mining\/","title":{"rendered":"What is Text Mining in Data Mining – Process & Applications"},"content":{"rendered":"
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1. Text Mining – Objective<\/span><\/h2>\n
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Through this Text Mining Tutorial, we will learn what is Text Mining, a process of Text Mining,\u00a0Text Mining Applications, approaches, issues, areas, and Advantages and Disadvantages of Text Mining.<\/p>\n<\/div>\n

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Text Mining in Data Mining – Concepts, Process & Applications<\/p><\/div>\n<\/div>\n

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2. What is Text Mining?<\/h2>\n<\/div>\n
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Text Mining is also known as Text Data Mining<\/em>. The purpose is too unstructured information, extract meaningful numeric indices from the text. Thus, make the information contained in the text accessible to the various algorithms. Information can extracte<\/span>\u00a0to derive summaries contained in the documents. Hence, you can analyze words, clusters of words used in documents. In the most general terms, text mining will “turn text into numbers”. Such as predictive data mining projects, the application of unsupervised learning methods.<\/div>\n<\/div>\n<\/div>\n
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Read more about Data Mining in detail<\/strong><\/a><\/div>\n
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3. Areas of Text Mining in Data Mining<\/h2>\n

Following are the areas of text mining in Data Mining:<\/p>\n

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Areas of Text Mining in Data Mining<\/p><\/div>\n<\/div>\n

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a. Information Retrieval (IR)<\/h3>\n<\/div>\n
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Information retrieval is regarded<\/span> as an extension to document retrieval. That the documents that are returned<\/span> are processed<\/span> to condense. Thus document retrieval\u00a0follow\u00a0by<\/span> a text summarization stage. That focuses on the query posed by the user. IR systems help in to narrow down the set of documents that are relevant to a particular problem. As text mining involves applying very complex algorithms to large document collections. Also, IR can speed up the analysis significantly<\/span> by reducing the number of documents.<\/div>\n<\/div>\n
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b. Data Mining (DM)<\/h3>\n<\/div>\n
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Data mining<\/strong> <\/a>can\u00a0loosely<\/span> describe\u00a0as looking for patterns in data. It can more characterize as the extraction of hidden from data. Data mining tools can predict behaviours and future trends. Also, it allows businesses to make positive, knowledge-based decisions. Data mining tools can answer business questions. Particularly that have traditionally<\/span> been too time-consuming to resolve. They search databases for hidden and unknown patterns.<\/div>\n<\/div>\n<\/div>\n
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Follow this link to know about Data Mining Tools<\/a><\/strong><\/div>\n
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c. Natural Language Processing (NLP)<\/h3>\n<\/div>\n
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NLP is one of the oldest and most challenging problems. It is the study of human language. So those computers can understand natural languages as humans do. NLP research pursues the vague question of how we understand the meaning of a sentence or a document. What are the indications we use to understand who did what to whom? The role of NLP in text mining is to deliver the system in the information extraction phase as an input.<\/div>\n<\/div>\n
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d. Information Extraction (IE)<\/h3>\n
Information Extraction is the task of automatically<\/span> extracting structured information from unstructured. In most of the cases, this activity includes processing human language texts by means of NLP.<\/div>\n<\/div>\n
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4. Text Mining Process<\/h2>\n<\/div>\n
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A process of Text mining involves a series of activities to perform<\/span>\u00a0to mine the information. These activities are:<\/div>\n<\/div>\n<\/div>\n
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The process of Text Mining<\/p><\/div>\n<\/div>\n

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a. Text Pre-processing<\/h3>\n<\/div>\n
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It involves a series of steps as shown in below:<\/div>\n<\/div>\n
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