metadata

license: apache-2.0
configs:
  - config_name: default
    data_files:
      - split: english_observations
        path: UX-comments_english_observations.jsonl
      - split: english_sentences
        path: UX-comments_english_sentences.jsonl
      - split: spanish_observations
        path: UX-comments_spanish_observations.jsonl
      - split: spanish_sentences
        path: UX-comments_spanish_sentences.jsonl
size_categories:
  - n<2K
task_categories:
  - text-classification
language:
  - en
  - es

Cross-Domain Polarity Models to Evaluate User eXperience in E-learning

Abstract

Virtual learning environments are growing in importance as fast as e-learning, which is becoming highly demanded by universities and students worldwide. This paper investigates how to automatically evaluate User eXperience in this domain using sentiment analysis techniques. For this purpose, a corpus has been built with the opinions of 583 users (107 English speakers and 476 Spanish speakers) about three learning management systems in different courses. All the collected opinions were manually labeled with polarity information (positive, negative, or neutral) by three human annotators, both at the whole opinion and sentence levels.

Dataset Information

	P	NEU	N	Total
Spanish Observations	338	53	85	476
Spanish Sentences	404	41	142	587
English Observations	56	21	30	107
English Sentences	90	14	80	184
P=Positive; NEU=Neutral; N=Negative

Citation Information

If you use this dataset, please cite the following paper:

@article{sanchis2021cross,
  title={Cross-Domain Polarity Models to Evaluate User eXperience in E-learning},
  author={Sanchis-Font, R. and Castro-Bleda, MJ. and Gonz{\'a}lez-Barba, J{\'A}. and Pla Santamar{\'\i}a, F. and Hurtado Oliver, LF.},
  journal={Neural Processing Letters},
  volume={53},
  pages={3199--3215},
  year={2021},
  doi={https://doi.org/10.1007/s11063-020-10260-5},
  url={http://hdl.handle.net/10251/176382},
}