Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain
Published 1 January 2020Open access
Dongmin Hyun, Junsu Cho, Hwanjo Yu
Citations8
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TL;DR
Large-scale datasets of users’ comments in two languages, English and Korean, are released for aspect-level sentiment analysis in automotive domain to enable users to analyze the voice of customers on automobiles.
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.
Keywords
Computer Science
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