@inproceedings{44d8ab6492d14548b40c71d845631a97,
title = "A Deep Learning Approach to Classify Aspect-Level Sentiment using Small Datasets",
abstract = "Sentiment analysis is an important technique to interpret user opinion on products from text, for example, as shared in social media. Recent approaches using deep learning can accurately extract overall sentiment from large datasets. However, extracting sentiment from specific aspects of a product with small training datasets remains a challenge. The automatic classification of sentiments at aspect-level can provide more detailed feedbacks about product and service opinions avoiding manual verification. In this work, we develop two deep learning approaches to classify sentiment at aspect-level using small datasets.",
keywords = "component, formatting, insert, style, styling",
author = "Aires, {Joao Paulo} and Carlos Padilha and Christian Quevedo and Felipe Meneguzzi",
note = "Funding Information: The authors would like to acknowledge Motorola Mobility for funding this research. Felipe thanks CNPq for partial financial support under its PQ fellowship, grant number 305969/2016-1. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Joint Conference on Neural Networks, IJCNN 2018 ; Conference date: 08-07-2018 Through 13-07-2018",
year = "2018",
month = oct,
day = "10",
doi = "10.1109/IJCNN.2018.8489760",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings",
}