Facebook Sentiment Analysis Using Python

Abstract

Individuals share their encounters, suppositions or essentially talk pretty much whatever worries them on the web. The extensive means of accessible information pulls in frame work engineers, considering of mining and investigation. Sentiment analysis has popularized due to the availability of abundant opinions that resides in social networks such as Facebook, Twitter, etc. Sentiments are published on these media inform of texts for expressing social support, happiness, anger, friendship etc. A Sentiment is frequently expressed in subtle and complex ways. In addition, data collected from World Wide Web often contains high noise. Sentiment Analysis is treated as a characterization undertaking as it groups the introduction of a content into either positive or negative. In this paper we present sentiment analysis using VADER (Valence Aware Dictionary and sEntiment Reasoner) is a sentiment analysis tool which is designed to analyze social media text and informal language. Unlike traditional sentiment analysis methods it is best at detecting sentiment in short pieces of text like tweets, product reviews or user comments which contain slang, emojis and abbreviations. It uses a pre-built lexicon of words associated with sentiment values and applies specific rules to calculate sentiment scores.