Owing to technological development, the internet has become the world's largest platform where an unaccountable amount of e-news information is freely available to use. Most of the time, e-newspaper readers have to examine the massive collection of e-news articles to locate necessary information relevant to them. Massive semi-structured and unstructured texts usually mislead the readers when they search and understand data for some knowledge. Furthermore, manually reading a collection of e-news articles for some knowledge is tedious and unproductive. The literature related to Knowledge Discovery from text documents has had a substantial improvement in this regard and Association Rule Extraction using text documents, in particular, has become a more frequent and imperative research approach to finding out the most significant information, patterns, and features in the text documents while diminishing the time for reading all the documents. This study provides a comprehensive review of Association Rule extraction using textual data covering the essential topics; Pre-processing, steps in Association Rule Mining, and rule mining algorithms. Out of the various existing association rule mining algorithms, the two most important algorithms, Apriori and FP Growth, are chosen for the experiment using e-news articles. Based on the experimental results, this study discusses the performance, significant bottlenecks, recent breakthroughs of rule mining algorithms, and finally the perspective directions to facilitate future research.