Multi-factor evaluation of teaching sentiment analysis in the new era

DOI

10.2991/978-94-6463-459-4_92How to use a DOI?

Keywords

Teaching evaluation; Affective disposition analysis; Semantic network analysis; LSTM empirical analysis

Abstract

Teaching reform constitutes a crucial task faced by universities in the new era. This paper analyzes existing issues in online course teaching modes and proposes recommendations for improving these modes. The present study focuses on multifactor evaluation of course instruction, selecting six factors as research subjects from a dataset provided by Kaggle website. We employ sentiment analysis, semantic network analysis, as well as LSTM-based sentiment analysis to delve into implementing online course education from students’ perspective while uncovering their concerns and learning needs, ultimately offering relevant suggestions. The conclusions drawn herein possess certain reference value for advancing online education.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yu Zhou
AU  - Chun Yan
PY  - 2024
DA  - 2024/07/23
TI  - Multi-factor evaluation of teaching sentiment analysis in the new era
BT  - Proceedings of the 2024 9th International Conference on Social Sciences and Economic Development (ICSSED 2024)
PB  - Atlantis Press
SP  - 827
EP  - 834
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-459-4_92
DO  - 10.2991/978-94-6463-459-4_92
ID  - Zhou2024
ER  -