Multi-label Classification of Twitter Data Using Modified ML-KNN

Abstract

Social media has become a very rich source of information. Labeling unstructured social media text is a critical task as features belong to multiple labels. Without appropriate labels, raw data does not make any sense. So it is mandatory to provide appropriate labels. In this work, we have proposed a modified multilabel K nearest neighbor (Modified ML-KNN) for generating multiple labels of tweets which when configured with a certain distance measure and number of nearest neighbors gives better performance than conventional ML-KNN. To validate the proposed approach, we have used two different twitter data sets, one Disease related tweets set prepared by us using five different disease keywords and an other benchmark Seattle data set consisting of incident-related tweets. The modified ML-KNN is able to improve the performance of conventional ML-KNN with a minimum of 5% in both the datasets.