Effective Sentiment Analysis Of Social Media Datasets Using Naive Bayesian Classification involves extraction of subjective information from textual data. A normal human can easily understand the sentiment of a document written in natural language based on its knowledge of understanding the polarity of words (unigram, bigram and n-grams) and in some cases the general semantics used to describe the subject. The project aims to make the machine extract the polarity (positive, negative or neutral) of social media dataset with respect to the queried keyword. This project introduces an approach for automatically classifying the sentiment of social media data by using the following procedure: First the training data is fed to the Sentiment Analysis Engine for learning by using machine learning algorithm. After the learning is complete with qualified accuracy, the machine starts accepting individual social data with respect to keyword that it analyses and interprets, and then classifies it as positive, negative or neutral with respect to the query term.

Natural Language Processing Machine Learning Supervised Learning Text Analysis