IUST at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using Deep Neural Networks and Linear Baselines

Soroush Javdan, Taha Shangipour ataei, Behrouz Minaei-Bidgoli


Abstract
Sentiment Analysis is a well-studied field of Natural Language Processing. However, the rapid growth of social media and noisy content within them poses significant challenges in addressing this problem with well-established methods and tools. One of these challenges is code-mixing, which means using different languages to convey thoughts in social media texts. Our group, with the name of IUST(username: TAHA), participated at the SemEval-2020 shared task 9 on Sentiment Analysis for Code-Mixed Social Media Text, and we have attempted to develop a system to predict the sentiment of a given code-mixed tweet. We used different preprocessing techniques and proposed to use different methods that vary from NBSVM to more complicated deep neural network models. Our best performing method obtains an F1 score of 0.751 for the Spanish-English sub-task and 0.706 over the Hindi-English sub-task.
Anthology ID:
2020.semeval-1.170
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
COLING | SemEval
SIGs:
SIGSEM | SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1270–1275
Language:
URL:
https://aclanthology.org/2020.semeval-1.170
DOI:
10.18653/v1/2020.semeval-1.170
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.semeval-1.170.pdf