A Smart Sentimentality Analysis on Twitter

Abstract

Depression is a global health concern. Social networks allow the affected
population to share their experiences. Social media provides limitless opportunities to
share experiences with their best suggestion. In current scenarios and with available new
technologies, twitter can be used effectively for gathering information rather than
gathering information in traditional method. Twitter is a most popular online social
networking service that enable user to share and gain knowledge. This enabled us to
accurately represent user interactions by relying on the data’s semantic content.
Preprocessed tweets are stored in database and those tweets are identified and classified
whether it is user keywords related post using Support Vector Machine classification. The
user keywords can be predicted whether it is a best suggestion using polarity. To provide
an interactive automatic system which predicts the sentiment of the review/tweets of the
people posted in social media. This system deals with the challenges that appear in the
process of Sentiment Analysis, real time tweets are considered as they are rich sources of
data for opinion mining and sentiment analysis. The main objective of this system is to
perform real time sentimental analysis on the tweets that are extracted from the twitter
and provide time based analytics to the user.