Social Media Application with Sentiment Analysis

Abstract

Social media is an attractive term for the netizens of this new generation. The net surfers go-on doing a lot many things on social media applications such as updating their profile, frequent posts of their images on the social media, sharing stories of their tours and travel, events etc.  Social media is a user-driven platform which gets bulk of its images, posts and videos uploaded by the user. Twitter, Facebook, WhatsApp and Instagram are the giant players of this industry. But with time these platforms need to evolve in accordance with the user preferences and market trends. For understanding the preferences of the user several methods are followed such as user feedbacks, discovering patterns and correlations in the user history and one another method is sentiment analysis. Sentiment analysis is a subset of text mining Sentiment analysis is extracting useful insights from the user data, like what they share on the social platform, what posts they make. This analysis of user sentiment helps in understanding the opinion of the user and also the emotions that prevail on the social media, changing with time and place. Users express their opinions and share their feelings on a number of events, places and products. Sentiment analysis is further classified at the document, sentence and aspect-level. It helps in recognizing the emotions of the humans. People vary widely in their accuracy at recognizing the emotions of others.These social media applications power the users to become member of certain communities and express their opinions and interests in events, posts and updates in those communities which in turn helps in identifying and understanding the users in much better way. The primary aim of this research paper is to propose a social media application that analyses the sentiment of the users from various social media platforms such as Facebook, Twitter and Google and then presents the useful insights into user’s data that helps in future evolvement of the platform.

Social media is an attractive term for the netizens of this new generation. The net surfers go-on doing a lot many things on social media applications such as updating their profile, frequent posts of their images on the social media, sharing stories of their tours and travel, events etc.  Social media is a user-driven platform which gets bulk of its images, posts and videos uploaded by the user. Twitter, Facebook, WhatsApp and Instagram are the giant players of this industry. But with time these platforms need to evolve in accordance with the user preferences and market trends. For understanding the preferences of the user several methods are followed such as user feedbacks, discovering patterns and correlations in the user history and one another method is sentiment analysis. Sentiment analysis is a subset of text mining Sentiment analysis is extracting useful insights from the user data, like what they share on the social platform, what posts they make. This analysis of user sentiment helps in understanding the opinion of the user and also the emotions that prevail on the social media, changing with time and place. Users express their opinions and share their feelings on a number of events, places and products. Sentiment analysis is further classified at the document, sentence and aspect-level. It helps in recognizing the emotions of the humans. People vary widely in their accuracy at recognizing the emotions of others.These social media applications power the users to become member of certain communities and express their opinions and interests in events, posts and updates in those communities which in turn helps in identifying and understanding the users in much better way. The primary aim of this research paper is to propose a social media application that analyses the sentiment of the users from various social media platforms such as Facebook, Twitter and Google and then presents the useful insights into user’s data that helps in future evolvement of the platform.