This repository contains corpus called 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 for as a training or test corpus for aspect-oriented sentiment analysis. Moreover, the corpus can benefit building inclusive public transportation systems.
The data is provided in XML-format, following the format described by "GermEval Task 2017 - Shared Task on Aspect-based Sentiment in Social
This data is distributed under the CC-BY 4.0 license.
Send pull requests to submit annotation amendments.
Please cite the following publication:
@InProceedings{gabryszak-thomas:2022:CSRNLP1,
author = {Gabryszak, Aleksandra and Thomas, Philippe},
title = {MobASA: Corpus for Aspect-based Sentiment Analysis and Social Inclusion in the Mobility Domain},
booktitle = {Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {35--39},
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},
url = {http://www.lrec-conf.org/proceedings/lrec2022/workshops/CSRNLP1/pdf/2022.csrnlp1-1.5.pdf}
}