information extraction sentiment and emotion analysis topic segmentation using techniques from nlp and text analyticsyou can work on text data to processannotateclassifyclustersummarizeextract semanticsdetermine sentimentand much morethe following example snippet depicts some basic nlp operations on textual data where we annotate document (text sentencewith various components like parts of speechphrase level tagsand so on based on its constituent grammar you can refer to page of text analytics with python (apressdipanjan sarkar for more details on constituency parsing from nltk parse stanford import stanfordparser sentence 'the quick brown fox jumps over the lazy dogcreate parser object scp stanfordparser(path_to_jar=' :/stanford/stanford-parser-full-/stanfordparser jar'path_to_models_jar=' :/stanford/stanford-parser-full-/stanfordparser--models jar'get parse tree result list(scp raw_parse(sentence)tree result[ in [ ]print the constituency parse tree print(tree(root (np (np (dt the(jj quick(jj brown(nn fox)(np (np (nns jumps)(pp (in over(np (dt the(jj lazy(nn dog)))))in [ ]visualize constituency parse tree tree draw(