Topic Modelling: A Comparative Study for Short Text

Sara Lasri, El Habib Nfaoui

2021

Abstract

Massive amounts of short text collected every day. Therefore, the challenging goal is to find the information we are looking for, so we need to organize, search, classify and understand this large quantity of data. Topic modelling is a better performing technique to solve this problem. Topic modelling provides us with methods to organize, understand and summarize the short categorical text.TM is an intuitive approach to extract the most essential topics detection in a short text

Paper Citation

in Harvard Style

Lasri S. and Nfaoui E. (2021). Topic Modelling: A Comparative Study for Short Text. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 479-482. DOI: 10.5220/0010737000003101

in Bibtex Style

@conference{bml21,
author={Sara Lasri and El Habib Nfaoui},
title={Topic Modelling: A Comparative Study for Short Text},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={479-482},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010737000003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Topic Modelling: A Comparative Study for Short Text
SN - 978-989-758-559-3
AU - Lasri S.
AU - Nfaoui E.
PY - 2021
SP - 479
EP - 482
DO - 10.5220/0010737000003101