Abstract
Text classification, document cluster, and similar endeavors area unit important analysis spaces underpinning a large vary of problems and applications within the area of internet intelligence. A typical approach, once cluster or otherwise classifying documents algorithmically, is to represent an online document as a vector of numbers derived from the frequencies of words therein document, wherever the words area unit taken from the complete assortment of documents into consideration. A tried approach is to use an inventory of “stop” words (also known as a ‘stop list’), that establish frequent words like ‘and’ and ‘or’ which might be unlikely to Words within the stop list area unit thus not enclosed within the vector that represents a document. Current stop lists area unit arguably noncurrent within the lightweight of fluctuations in word usage, and conjointly arguably innocent of sure candidate generic stop lists, thus delivery into question that we tend to explore this by developing new stop lists in an exceedingly rule based method.
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