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Many words in documents recur very frequently but are essentially meaninglessas they are used to join words together in a sentence. It is commonlyunderstood that stop words do not contribute to the context or content oftextual documents. Due to their high frequency of occurrence, their presence intext mining presents an obstacle to the understanding of the content in thedocuments. To eliminate the bias effects, most text mining software orapproaches make use of stop words list to identify and remove those words.However, the development of such top words list is difficult and inconsistentbetween textual sources. This problem is further aggravated by sources such asTwitter which are highly repetitive or similar in nature. In this paper, wewill be examining the original work using term frequency, inverse documentfrequency and term adjacency for developing a stop words list for the Twitterdata source. We propose a new technique using combinatorial values as analternative measure to effectively list out stop words.

Effective Listings of Function Stop words for Twitter