TueMix at SemEval-2020 Task 9: Logistic Regression with Linguistic Feature Set

Elizabeth Bear, Diana Constantina Hoefels, Mihai Manolescu


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.178.pdf
Data
SentiMix