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Maia, R., Ferreira, J. & Martins, A. (2019). Rating prediction on yelp academic dataset using paragraph vectors. In Proceedings of 232nd The IIER International Conference. (pp. 56-62).: IIER.

Rui et al.,  "Rating prediction on yelp academic dataset using paragraph vectors", in Proc. of 232nd The IIER Int. Conf., IIER, 2019, pp. 56-62

@inproceedings{rui2019_1714719748174,
	author = "Maia, R. and Ferreira, J. and Martins, A.",
	title = "Rating prediction on yelp academic dataset using paragraph vectors",
	booktitle = "Proceedings of 232nd The IIER International Conference",
	year = "2019",
	editor = "",
	volume = "",
	number = "",
	series = "",
	pages = "56-62",
	publisher = "IIER",
	address = "",
	organization = "",
	url = "http://worldresearchlibrary.org/proceeding.php?pid=2815"
}

TY  - CPAPER
TI  - Rating prediction on yelp academic dataset using paragraph vectors
T2  - Proceedings of 232nd The IIER International Conference
AU  - Maia, R.
AU  - Ferreira, J.
AU  - Martins, A.
PY  - 2019
SP  - 56-62
SN  - 2348-7437
UR  - http://worldresearchlibrary.org/proceeding.php?pid=2815
AB  - This work studies the application of Paragraph Vectors to the Yelp Academic Dataset reviews in order to predict user ratings for different categories of businesses like auto repair, restaurants or veterinarians. Paragraph Vectors is a word embeddings techniques were each word or piece of text is converted to a continuous low dimensional space. Then, the opinion mining or sentiment analysis is observed as a classification task, where each user review is associated with a label the rating - and a probabilistic model is built with a logistic classifier. Following the intuition that the semantic information present in textual user reviews is generally more complex and complete than the numeric rating itself, this work applies Paragraph Vectors successfully toYelp dataset and evaluates its results. 
ER  -