Twitter功能停用词的有效列表
Effective Listings of Function Stop words for Twitter
摘要：
文档中的许多单词经常出现，但基本上没有意义，因为它们用于将单词连接在一起组成句子。通常认为停用词对文本文档的上下文或内容没有贡献。由于它们出现的频率很高，它们在文本挖掘中的存在对理解文档中的内容构成了障碍。为了消除偏见效应，大多数文本挖掘软件或方法使用停止词列表来识别和删除这些词。然而，这样的顶级词汇列表的开发是困难的，并且在文本来源之间不一致。Twitter等来源的高度重复或性质相似使这一问题进一步恶化。在本文中，我们将使用词频，逆文档频率和术语邻接来检查原始工作，以开发Twitter数据源的停用词列表。我们提出了一种新的技术，使用组合值作为一种替代措施，有效地列出停止字。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
