Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain

Dongmin Hyun; Junsu Cho; Hwanjo Yu

COLING 2020main17 citations

Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain

Abstract

We release large-scale datasets of users’ comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain. The datasets consist of 58,000+ commentaspect pairs, which are the largest compared to existing datasets. In addition, this work covers new language (i.e., Korean) along with English for aspect-level sentiment analysis. We build the datasets from automotive domain to enable users (e.g., marketers in automotive companies) to analyze the voice of customers on automobiles. We also provide baseline performances for future work by evaluating recent models on the released datasets.

BibTeX

@inproceedings{hyun-etal-2020-building,
    title = "Building Large-Scale {E}nglish and {K}orean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain",
    author = "Hyun, Dongmin  and
      Cho, Junsu  and
      Yu, Hwanjo",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2020.coling-main.83/",
    doi = "10.18653/v1/2020.coling-main.83",
    pages = "961--966"
}