Abstract
Commonly occurring in settings such as social media platforms, code-mixed content makes the task of identifying sentiment notably more challenging and complex due to the lack of structure and noise present in the data. SemEval-2020 Task 9, SentiMix, was organized with the purpose of detecting the sentiment of a given code-mixed tweet comprising Hindi and English. We tackled this task by comparing the performance of a system, TueMix - a logistic regression algorithm trained with three feature components: TF-IDF n-grams, monolingual sentiment lexicons, and surface features - with a neural network approach. Our results showed that TueMix outperformed the neural network approach and yielded a weighted F1-score of 0.685.- Anthology ID:
- 2020.semeval-1.178
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 1316–1321
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.178
- DOI:
- 10.18653/v1/2020.semeval-1.178
- Cite (ACL):
- Elizabeth Bear, Diana Constantina Hoefels, and Mihai Manolescu. 2020. TueMix at SemEval-2020 Task 9: Logistic Regression with Linguistic Feature Set. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1316–1321, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- TueMix at SemEval-2020 Task 9: Logistic Regression with Linguistic Feature Set (Bear et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.semeval-1.178.pdf
- Data
- SentiMix