text mining and text analytics showing your results in an original way can be key to successful project people never appreciate the effort you've put into achieving your results if you can' communicate them and they're meaningful to them an original data visualization here and there certainly helps with this summary text mining is widely used for things such as entity identificationplagiarism detectiontopic identificationtranslationfraud detectionspam filteringand more python has mature toolkit for text mining called nltkor the natural language toolkit nltk is good for playing around and learning the ropesfor real-life applicationshoweverscikit-learn is usually considered more "production-ready scikit-learn is extensively used in previous the data preparation of textual data is more intensive than numerical data preparation and involves extra techniquessuch as stemming--cutting the end of word in smart way so it can be matched with some conjugated or plural versions of this word lemmatization--like stemmingit' meant to remove doublesbut unlike stemmingit looks at the meaning of the word stop word filtering--certain words occur too often to be useful and filtering them out can significantly improve models stop words are often corpusspecific tokenization--cutting text into pieces tokens can be single wordscombinations of words ( -grams)or even whole sentences pos tagging--part-of-speech tagging sometimes it can be useful to know what the function of certain word within sentence is to understand it better in our case study we attempted to distinguish reddit posts on "game of thronesversus posts on "data science in this endeavor we tried both the naive bayes and decision tree classifiers naive bayes assumes all features to be independent of one anotherthe decision tree classifier assumes dependencyallowing for different models in our examplenaive bayes yielded the better modelbut very often the decision tree classifier does better jobusually when more data is available we determined the performance difference using confusion matrix we calculated after applying both models on new (but labeleddata when presenting findings to other peopleit can help to include an interesting data visualization capable of conveying your results in memorable way