Suicidal Trend Analysis of Twitter Using Machine Learning and Neural Network

Abstract

Increasing number of social networking sites made people more engaged in their virtual life more than ever and at the same time amount data people put online is enormous and also heaven for researchers for conducting their researches. People tend to put their thoughts online to share with the whole world which also includes suicidal thoughts. Suicide is a social problem and is a major concern of recent times. In this research paper we mainly focused on Twitter which is one of the well-known networking sites. We adopted an approach of machine learning and neural network for this research. Support Vector Machine (SVM) is one of the best machine leaning algorithm for text analysis and neural network is also well known for performance in complex cases. In case of neural network we used three types weight optimizers namely Limited-memory BFGS, Stochastic gradient descent and an extension of stochastic gradient descent which is Adam to attain maximum accuracy. At the end we were able to attain accuracy of 95.2% using SVM and 97.6% using neural network and also we performed 10 fold cross validation for further model performance evaluation.

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