This article shows how you can perform sentiment analysis on Twitter tweets using Python and Natural Language Toolkit (NLTK).
Sentiment Analysis means analyzing the sentiment of a given text or document and categorizing the text/document into a specific class or category (like positive and negative). In other words, we can say that sentiment analysis classifies any particular text or document as positive or negative. Basically, the classification is done for two classes: positive and negative. However, we can add more classes like neutral, highly positive, highly negative, etc.

This article shows how you can classify text into different categories using Python and Natural Language Toolkit (NLTK).
Examples of text classification include spam filtering, sentiment analysis (analyzing text as positive or negative), genre classification, categorizing news articles, etc. There can be some categories and we need to do text analysis and classify the text/document into one of the available categories.

This article shows how you can do Part-of-Speech Tagging of words in your text document in Natural Language Toolkit (NLTK).
Part-of-Speech Tagging means classifying word tokens into their respective part-of-speech and labeling them with the part-of-speech tag.

This article shows how you can use the WordNet lexical database in NLTK (Natural Language Toolkit).
We deal with basic usage of WordNet and also finding synonyms, antonyms, hypernyms, hyponyms, holonyms of words. We also look into finding the similarities between any two words.