Identification of Suicidal Intent Using Machine Learning Techniques over Twitter Data

Abstract

Abstract: Machine learning based on categorical classification has integrated usages in a variety of fields like prediction, finance, supply chain management, sales and operations as well as product analytics. This study shows how Support Vector Machine Learning Model from the Supervised Learning sub-branch of Machine Learning predicts the suicidal intent of a person’s “tweet” on the social media platform ‘Twitter’. This model basically indicates whether the text tweeted or posted by a person may be suicidal or not. Regularised data set for Modeling is divided into test data set and training data set at the rate of 3:7. Machine Learning for this study needs to use Pandas, Numpy , Pillow, ScikitLearn, Textblob and Nltk frameworks. Massive amount of actual Twitter data is used as a dataset for the training and testing purpose so the model can analyse the text with maximum accuracy