Effective Listings of Function Stop words for Twitter
This work addresses the challenge of text mining in repetitive sources like Twitter, offering a domain-specific improvement for data preprocessing.
The paper tackles the problem of inconsistent stop word lists for Twitter by proposing a new technique using combinatorial values, achieving a more effective identification of stop words compared to traditional methods like term frequency and inverse document frequency.
Many words in documents recur very frequently but are essentially meaningless as they are used to join words together in a sentence. It is commonly understood that stop words do not contribute to the context or content of textual documents. Due to their high frequency of occurrence, their presence in text mining presents an obstacle to the understanding of the content in the documents. To eliminate the bias effects, most text mining software or approaches make use of stop words list to identify and remove those words. However, the development of such top words list is difficult and inconsistent between textual sources. This problem is further aggravated by sources such as Twitter which are highly repetitive or similar in nature. In this paper, we will be examining the original work using term frequency, inverse document frequency and term adjacency for developing a stop words list for the Twitter data source. We propose a new technique using combinatorial values as an alternative measure to effectively list out stop words.