MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain

Aleksandra Gabryszak, Philippe Thomas


Abstract
In this paper we show how aspect-based sentiment analysis might help public transport companies to improve their social responsibility for accessible travel. We present MobASA: a novel German-language corpus of tweets annotated with their relevance for public transportation, and with sentiment towards aspects related to barrier-free travel. We identified and labeled topics important for passengers limited in their mobility due to disability, age, or when travelling with young children. The data can be used to identify hurdles and improve travel planning for vulnerable passengers, as well as to monitor a perception of transportation businesses regarding the social inclusion of all passengers. The data is publicly available under: https://github.com/DFKI-NLP/sim3s-corpus
Anthology ID:
2022.csrnlp-1.5
Volume:
Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
CSRNLP
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
35–39
Language:
URL:
https://aclanthology.org/2022.csrnlp-1.5
DOI:
Bibkey:
Cite (ACL):
Aleksandra Gabryszak and Philippe Thomas. 2022. MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain. In Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference, pages 35–39, Marseille, France. European Language Resources Association.
Cite (Informal):
MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain (Gabryszak & Thomas, CSRNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.csrnlp-1.5.pdf
Code
 dfki-nlp/sim3s-corpus