We live in an era of digital data explosion over Internet. Data warehouses deal with numerical databases than textual sources. Nearly eighty percent of digital data is either in semi or un-structured textual form. Several knowledge mining techniques developed over the past decade and those that are being developed now continue to draw attention to transform such textual data into desirable information and useful knowledge. This knowledge and information is used to benefit many fields of applications such as: social network, business management, customer care management system, market analysis, search engines, fraud detection, just to name a few. Text Mining (TM) is what is needed if desired information is to be obtained from such voluminous data. TM is multi-disciplinary in nature. Several TM techniques are deployed in the process of extracting knowledge from textual sources. Input text for such techniques needs to be pre-processed and cleaned. This survey briefly presents pre-processing tools for TM in general and Natural Language Processing (NLP) in particular. Also presents the broad categories of TM techniques used. The focus of this paper is to explore and analyze several features of text preprocessing techniques and tools that would interest researchers in the area of TM.

Text Mining, Pre-processing techniques, Pre-processing Tools, Natural Language Processing

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