@inproceedings{aparaschivei-etal-2020-fii,
title = "{FII}-{UAIC} at {S}em{E}val-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using {CNN}",
author = "Aparaschivei, Lavinia and
Palihovici, Andrei and
G{\^\i}fu, Daniela",
editor = "Herbelot, Aurelie and
Zhu, Xiaodan and
Palmer, Alexis and
Schneider, Nathan and
May, Jonathan and
Shutova, Ekaterina",
booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
month = dec,
year = "2020",
address = "Barcelona (online)",
publisher = "International Committee for Computational Linguistics",
url = "https://aclanthology.org/2020.semeval-1.118",
doi = "10.18653/v1/2020.semeval-1.118",
pages = "928--933",
abstract = "The {``}Sentiment Analysis for Code-Mixed Social Media Text{''} task at the SemEval 2020 competition focuses on sentiment analysis in code-mixed social media text , specifically, on the combination of English with Spanish (Spanglish) and Hindi (Hinglish). In this paper, we present a system able to classify tweets, from Spanish and English languages, into positive, negative and neutral. Firstly, we built a classifier able to provide corresponding sentiment labels. Besides the sentiment labels, we provide the language labels at the word level. Secondly, we generate a word-level representation, using Convolutional Neural Network (CNN) architecture. Our solution indicates promising results for the Sentimix Spanglish-English task (0.744), the team, Lavinia{\_}Ap, occupied the 9th place. However, for the Sentimix Hindi-English task (0.324) the results have to be improved.",
}
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%0 Conference Proceedings
%T FII-UAIC at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using CNN
%A Aparaschivei, Lavinia
%A Palihovici, Andrei
%A Gîfu, Daniela
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y May, Jonathan
%Y Shutova, Ekaterina
%S Proceedings of the Fourteenth Workshop on Semantic Evaluation
%D 2020
%8 December
%I International Committee for Computational Linguistics
%C Barcelona (online)
%F aparaschivei-etal-2020-fii
%X The “Sentiment Analysis for Code-Mixed Social Media Text” task at the SemEval 2020 competition focuses on sentiment analysis in code-mixed social media text , specifically, on the combination of English with Spanish (Spanglish) and Hindi (Hinglish). In this paper, we present a system able to classify tweets, from Spanish and English languages, into positive, negative and neutral. Firstly, we built a classifier able to provide corresponding sentiment labels. Besides the sentiment labels, we provide the language labels at the word level. Secondly, we generate a word-level representation, using Convolutional Neural Network (CNN) architecture. Our solution indicates promising results for the Sentimix Spanglish-English task (0.744), the team, Lavinia_Ap, occupied the 9th place. However, for the Sentimix Hindi-English task (0.324) the results have to be improved.
%R 10.18653/v1/2020.semeval-1.118
%U https://aclanthology.org/2020.semeval-1.118
%U https://doi.org/10.18653/v1/2020.semeval-1.118
%P 928-933
Markdown (Informal)
[FII-UAIC at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using CNN](https://aclanthology.org/2020.semeval-1.118) (Aparaschivei et al., SemEval 2020)
ACL