text mining and text analytics figure google maps asks you for more context due to the ambiguity of the query "springfield another problem is spelling mistakes and different (correctspelling forms of word take the following three references to new york"ny,"neww york,and "new york for humanit' easy to see they all refer to the city of new york because of the way our brain interprets textunderstanding text with spelling mistakes comes naturally to uspeople may not even notice them but for computer these are unrelated strings unless we use algorithms to tell it that they're referring to the same entity related problems are synonyms and the use of pronouns try assigning the right person to the pronoun "shein the next sentences"john gave flowers to marleen' parents when he met her parents for the first time she was so happy with this gesture easy enoughrightnot for computer we can solve many similar problems with easebut they often prove hard for machine we can train algorithms that work well on specific problem in welldefined scopebut more general algorithms that work in all cases are another beast altogether for instancewe can teach computer to recognize and retrieve us account numbers from textbut this doesn' generalize well to account numbers from other countries language algorithms are also sensitive to the context the language is used ineven if the language itself remains the same english models won' work for arabic and vice versabut even if we keep to english--an algorithm trained for twitter data isn' likely to perform well on legal texts let' keep this in mind when we move on to the case studythere' no perfectone-size-fits-all solution in text mining