The three articles introduce the definition of text mining, its features, and different specific techniques. The most significant use of text mining nowadays is the search engine. When users type keywords in the search bar, the computer system uses text mining to collect related information. Furthermore, the current Google search not only includes the technique of text mining but also information in various formats, including images, videos, and shopping links. Text mining uses the topic-modeling to identify related texts, while image search utilizes AI technology to define elements and colors of pictures to collect similar images. Besides, text mining can also be used in customer service to gather consumer feedback and get to know their main pain points. By analyzing the keywords of customers’ feedback, the company can quickly learn what consumers are unsatisfied with to come up with instant solutions. Although letting consumers grade the service by rating can also showcase the level of satisfaction, companies still need more information about the reasons behind the rating. In terms of the shortcomings of text mining, just as mentioned in the reading named Alien Reading: Text Mining, Language Standardization, and the Humanities, computers are not able to account for the nuances of literary language due to the inability of sentiment. Computers can quite well understand scientific texts without much sentiment. However, in some types of literacy, words can not be interpreted without understanding the whole context. As a result, as mentioned in the reading called Untangling Text Data Mining, we do not need wholly artificial intelligent text analysis; rather, a mixture of computationally-driven and user-guided analysis may open the door to exciting new results.