Author: Luong Luc Phan et al., 2021
Institute: University of Information Technology-VNUHCM, Vietnam
Email: 18521073@gm.uit.edu.vn
Link paper: link
Please cite to the following conference when using the dataset:
@InProceedings{10.1007/978-3-030-82147-0_53,
author="Luc Phan, Luong
and Huynh Pham, Phuc
and Thi-Thanh Nguyen, Kim
and Khai Huynh, Sieu
and Thi Nguyen, Tham
and Thanh Nguyen, Luan
and Van Huynh, Tin
and Van Nguyen, Kiet",
editor="Qiu, Han
and Zhang, Cheng
and Fei, Zongming
and Qiu, Meikang
and Kung, Sun-Yuan",
title="SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence",
booktitle="Knowledge Science, Engineering and Management ",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="647--658",
abstract="In this paper, we present a process of building a social listening system based on aspect-based sentiment analysis in Vietnamese, from creating a dataset to building a real application. Firstly, we create UIT-ViSFD, a Vietnamese Smartphone Feedback Dataset, as a new benchmark dataset built based on a strict annotation scheme for evaluating aspect-based sentiment analysis, consisting of 11,122 human-annotated comments for mobile e-commerce, which is freely available for research purposes. We also present a proposed approach based on the Bi-LSTM architecture with the fastText word embeddings for the Vietnamese aspect-based sentiment task. Our experiments show that our approach achieves the best performances (in F1-score) of 84.48{\%} for the aspect task and 63.06{\%} for the sentiment task, which performs several conventional machine learning and deep learning systems. Lastly, we build SA2SL, a social listening system based on the best performance model on our dataset, which will inspire more social listening systems in the future.",
isbn="978-3-030-82147-0"
}
If any company utilizes the dataset for commercial purposes, please contact us via email: 18521073@gm.uit.edu.vn.
We crawl textual feedback from customers about smartphones on a large e-commerce website in Vietnam. The label of the dataset is ten aspects and three polarities. Please read the guidelines in the paper for more information. We randomly divide the dataset into three sets:
- Train: 7,786.
- Dev: 1,112.
- Test: 2,224.
The dataset consists of 11,122 comments, including of four features:
- comment: Commentary content.
- n_star: The user evaluates the smartphone's star.
- data_time: The date and time the comment was posted.
- label: Label of comment.
All samples are in text format. No tokenization has been applied. Users of this dataset are free to use whatever sentence representation they choose.