Sentimental Analysis is the process of computationally determining whether a piece of writing is positive, negative, or neutral. It is also known as opinion mining, deriving the opinion or attitude of the speaker. Twitter with comparison: Tweets are collected using the twitter API in python. We select the best qualifier by testing on a dataset and if a tweet contains qualifiers like ‘better’ or ‘worse’ it accordingly calculates the accuracy of sentiment. The objectives of this project are : 1.) Performing sentiment analysis on twitter data. 2.) Extracting sentiment and gauging popularity of people in twitter. This project is done to predict the attitude of tweets from the social media site “Twitter”.
NAÏVE BEYES
It is an approach particularly suited when the dimensionality of the inputs is high. It is used to predict the probability for a given words to belong to a particular class. It is used because of its easiness in both during training and classifying steps. Pre-processed data is given as input to train input set using Naïve Bayes classifier and that trained model is applied on test to generate either positive or negative sentiment.