Twitter Sentiment Analysis

Abstract

Sentiment analysis deals with relating and classifying opinions or sentiments expressed in source textbook. A significant quantum of sentiment-rich data is being produced via social media in the form of tweets, status updates, blog entries, and other content. Understanding the opinions of the millions can be greatly served from sentiment analysis of this stoner- generated data. Twitter sentiment analysis is more grueling than general sentiment analysis because of the frequence of misspellings and shoptalk expressions. Twitter allows a character count of over to 140 characters. The two approaches employed to assay sentiments from the textbook are the knowledge base fashion and the machine literacy approach. Analysing sentiments help in understanding how people are allowing emotionally and classifying it as negative, positive or neutral. The dataset used is a collection of tweets related to the brand apple. Two different machine learning classifiers are used then, so that a person's sentiment can be linked. These classifiers are applied and also the stylish classifier with the stylish result will be chosen in order to prognosticate people's feelings. Professionals will be better suitable to assess people's feelings and fete early signs of torture through this analysis.