chunking chunking is process which is similar to parsing or tokenization but the major difference is that instead of trying to parse each wordwe will target phrases present in the document consider the sentence "the brown fox saw the yellow dogin this sentencewe have two phrases which are of interest the first is the phrase "the brown fox,which is noun phrase and the second one is the phrase "the yellow dog,which again is noun phrase by using the process of chunkingwe are able to tag phrases with additional parts of speech informationwhich is important for understanding the structure of the document the nltk module nltk chunk consists of necessary techniques that can be used for applying the chunking process to our corpora  entiment sentiment or emotion analysis is one of the most recognizable applications on text data sentiment analysis is the process of taking text document and trying to determine the opinion and polarity being represented by that document polarity in the reference of text document can mean the emotione positivenegativeor neutral being represented by the data the sentiment analysis on textual data can be done using different algorithms and at different levels of text segmentation the nltk sentiment package is the module that can be used to perform different sentiment analyses on text documents check out for real-world case study on sentiment analysis lassification/clustering classification of text documents is supervised learning problemas we explained in the first classification of text documents may involve learning the sentimenttopicthemecategoryand so on of several text documents (corpusand then using the trained model to label unknown documents in the future the major difference from normal structured data comes in the form of feature representations of unstructured text we will be using clustering involves grouping together similar documents based on some similarity measurelike cosine similaritybm distanceor even semantic similarity the nltk classify and nltk cluster modules are typically used to perform these operations once we do the necessary feature engineering and extraction other text analytics frameworks typicallynltk is our go-to library for dealing with text databut the python ecosystem also contains other libraries that can be useful in dealing with textual data we will briefly mention some of these libraries so that you get good grasp of the toolkit that you can arm yourself with when dealing with unstructured textual data patternthe pattern framework is web mining module for the python programming language it has tools for web mining (extracting data from googletwittera web crawleror an html dom parser)information retrievalnlpmachine learningsentiment analysis and network analysisand visualization unfortunatelypattern currently works best on python and there is no official port for python gensimthe gensim frameworkwhich stands for generate similaris python library that has core purpose of topic modeling at scalethis can be used to extract semantic topics from documents the focus of gensim is on providing efficient topic modeling and similarity analysis it also contains python implementation of google' popular word vec model