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Lopes-Teixeira, D., Batista, F. & Ribeiro, R. (2018).  Discovering trends in brand interest through topic models . In 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. (pp.  245-252 ). Sevilha:  SciTePress .

D. Lopes-Teixeira et al.,  " Discovering trends in brand interest through topic models ", in 10th Int. Joint Conf. on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Sevilha,  SciTePress , 2018, pp.  245-252 

@inproceedings{lopes-teixeira2018_1714806527862,
	author = "Lopes-Teixeira, D. and Batista, F. and Ribeiro, R.",
	title = " Discovering trends in brand interest through topic models ",
	booktitle = "10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",
	year = "2018",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.5220/0006936202450252",
	pages = " 245-252 ",
	publisher = " SciTePress ",
	address = "Sevilha",
	organization = " INSTICC ",
	url = "http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006936202450252"
}

TY  - CPAPER
TI  -  Discovering trends in brand interest through topic models 
T2  - 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
AU  - Lopes-Teixeira, D.
AU  - Batista, F.
AU  - Ribeiro, R.
PY  - 2018
SP  -  245-252 
DO  - 10.5220/0006936202450252
CY  - Sevilha
UR  - http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006936202450252
AB  - 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 ca pture those topics spiked by real-life events.
ER  -