A Short Text Clustering Approaches in Social Media

Abstract

In general, handling bag-of-words representation or TF-IDF is an arduous task with short text clustering as it results in sparse vector representation mainly for short texts. This paper provides an in-depth study on learning predictor features through an auto encoder model and sentence embedding technique. Assignment of user from any clustering technique as a process of supervision in order to update the weight of the encoder analysis. Short text related datasets validate of measure effective methods and algorithms for short text unsupervised data. Among this short text are not able to clustering in some social media. This paper discusses the challenge and methods to cluster in social media short text challenging issues. Clustering methods of algorithms to sort the problem to recovery the short text using K-means algorithms of Convolution Neural Network method. This paper provides a comprehensive review of short text prediction using the clustering algorithm, explore the research challenges, and open issues in this area.