With the advancement and growth of data mining there has been a great scope and requirement of an area which can provide the purpose of various domains. Mixture of schemes from data mining, language, information process retrieval and visual understanding, created an interdisciplinary field called text mining. Text mining is also commonly referred as the procedure of pulling out the useful information and sequence from a shapeless text. In order to get high text information, a process of pattern division and trends is done.
For an efficient text mining system, the shapeless texts are parsed, joined and / or separated to some level of linguistic feature, thus making it an ordered text, so that the same may be analyzed and studied, so as to form a certain opinion and understanding regarding a certain issue. A standard mining approach most importantly involves classification of the text, banding and pulling out of idea from the same, coarse catalog creation, response analysis, transcript review and modeling.
Text mining involves a two stage processing of text.
In the first step: A sketch of the document and its content is done. This procedure is called categorization process.
In the second step: The document is sorted into descriptive category and an inter document association is established. This procedure is called as classification process.
Of late, content mining has become useful in many areas, i.e. safety applications, software applications, educational applications etc. In the aggressive world of business, there is a hunt to clutch the pie of content mining benefits. With most of the companies concentrating on customer relationship management, the need for a method to study the customer feedback in an efficient and effective way is in high demand. This is where text mining fills in the blank. Companies usually focus in a smaller quantitative image of customer response and thus ignoring a broader surface of CRM. Moreover, people dealing with and running the CRM, do not pay much attention to the everyday interactions, proposals, grievances and praises.
Resultantly, there is a further decline in the Customer Relationship Analysis. With the use of content mining, a link to the behavioral statistics can easily be acquired, which undoubtedly provides us an additional benefit to the standard numerical analysis. With the association of the text mining, which, in itself involves artificial intelligence, machine learning and statistical analysis can be very helpful in forecasting and calculating the future course of customer relationship management.