@inproceedings{herath-etal-2022-dataset,
title = "Dataset and Baseline for Automatic Student Feedback Analysis",
author = "Herath, Missaka and
Chamindu, Kushan and
Maduwantha, Hashan and
Ranathunga, Surangika",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.219",
pages = "2042--2049",
abstract = "In this paper, we present a student feedback corpus, which contains 3000 instances of feedback written by university students. This dataset has been annotated for aspect terms, opinion terms, polarities of the opinion terms towards targeted aspects, document-level opinion polarities and sentence separations. We develop a hierarchical taxonomy for aspect categorization, which covers all the areas of the teaching-learning process. We annotated both implicit and explicit aspects using this taxonomy. Annotation methodology, difficulties faced during the annotation, and the details about the aspect term categorization have been discussed in detail. This annotated corpus can be used for Aspect Extraction, Aspect Level Sentiment Analysis, and Document Level Sentiment Analysis. Also the baseline results for all three tasks are given in the paper.",
}
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%0 Conference Proceedings
%T Dataset and Baseline for Automatic Student Feedback Analysis
%A Herath, Missaka
%A Chamindu, Kushan
%A Maduwantha, Hashan
%A Ranathunga, Surangika
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F herath-etal-2022-dataset
%X In this paper, we present a student feedback corpus, which contains 3000 instances of feedback written by university students. This dataset has been annotated for aspect terms, opinion terms, polarities of the opinion terms towards targeted aspects, document-level opinion polarities and sentence separations. We develop a hierarchical taxonomy for aspect categorization, which covers all the areas of the teaching-learning process. We annotated both implicit and explicit aspects using this taxonomy. Annotation methodology, difficulties faced during the annotation, and the details about the aspect term categorization have been discussed in detail. This annotated corpus can be used for Aspect Extraction, Aspect Level Sentiment Analysis, and Document Level Sentiment Analysis. Also the baseline results for all three tasks are given in the paper.
%U https://aclanthology.org/2022.lrec-1.219
%P 2042-2049
Markdown (Informal)
[Dataset and Baseline for Automatic Student Feedback Analysis](https://aclanthology.org/2022.lrec-1.219) (Herath et al., LREC 2022)
ACL
- Missaka Herath, Kushan Chamindu, Hashan Maduwantha, and Surangika Ranathunga. 2022. Dataset and Baseline for Automatic Student Feedback Analysis. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2042–2049, Marseille, France. European Language Resources Association.