Abstract: - TheInternet has become a basic requirement for everybody. There is rapid increasein social network applications; people are using these platform to voice themtheir opinions with regard to daily issues. People reactions toward buying a product, public services, and so on arevital. In recent years, researchers inthe field of sentiment analysis have been concerned with Analyse in opinions ondifferent topics such as movies, commercial products, and daily societalissues. The main objective of our proposed system is to perform analysis ontweets having sentiment which causes the great help for predicting the future.This paper addresses the sentiment analysis on twitter dataset. We only usetweets with negative labels. The dataset is collected which contains tweets. Ithas three parts: a training set, a development set, and a test set. Our mainaim is to perform analysis on these tweets and conclude the tweets which are showssuicidal ideation. To accurately detect mentions of suicide ideation andWillingness to attempt suicide, our paper will explore the various SentimentAnalysis applied to the Social data. It gives an introduction to this fascinating problem and to present a frameworkwhich will perform sentiment analysis on twitter posts by associating somemachine learning algorithm such as NLP, Naive Bayes, etc. Posts that describesinformation about the person’sactivities and help to find out a tendency of that particular person towardsgreater stress and disease development is important because by using it we cancalculate depression level of user and according to it we will sendmotivational video links to them on their register contact information (mobilenumber, Gmail, etc.).
Keywords - Sentiment Analysis,Machine Learning, Social Platform, Depression Percentage.