Discovering Trends in Brand Interest through Topic Models

Diana Lopes-Teixeira, Fernando Batista, Ricardo Ribeiro

2018

Abstract

Topic Modeling is a well-known unsupervised learning technique used when dealing with text data. It is used to discover latent patterns, called topics, in a collection of documents (corpus). This technique provides a convenient way to retrieve information from unclassified and unstructured text. Topic Modeling tasks have been performed for tracking events/topics/trends in different domains such as academic, public health, marketing, news, and so on. In this paper, we propose a framework for extracting topics from a large dataset of short messages, for brand interest tracking purposes. The framework consists training LDA topic models for each brand using time intervals, and then applying the model on aggregated documents. Additionally, we present a set of preprocessing tasks that helped to improve the topic models and the corresponding outputs. The experiments demonstrate that topic modeling can successfully track people’s discussions on Social Networks even in massive datasets, and capture those topics spiked by real-life events.

Paper Citation

in Harvard Style

Lopes-Teixeira D., Batista F. and Ribeiro R. (2018). Discovering Trends in Brand Interest through Topic Models. In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 1: KDIR; ISBN 978-989-758-330-8, SciTePress, pages 245-252. DOI: 10.5220/0006936202450252

in Bibtex Style

@conference{kdir18,
author={Diana Lopes-Teixeira and Fernando Batista and Ricardo Ribeiro},
title={Discovering Trends in Brand Interest through Topic Models},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 1: KDIR},
year={2018},
pages={245-252},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006936202450252},
isbn={978-989-758-330-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018) - Volume 1: KDIR
TI - Discovering Trends in Brand Interest through Topic Models
SN - 978-989-758-330-8
AU - Lopes-Teixeira D.
AU - Batista F.
AU - Ribeiro R.
PY - 2018
SP - 245
EP - 252
DO - 10.5220/0006936202450252
PB - SciTePress