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        <title>TueMix at SemEval-2020 Task 9: Logistic Regression with Linguistic Feature Set</title>
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        <namePart type="given">Elizabeth</namePart>
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            <title>Proceedings of the Fourteenth Workshop on Semantic Evaluation</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T TueMix at SemEval-2020 Task 9: Logistic Regression with Linguistic Feature Set
%A Bear, Elizabeth
%A Hoefels, Diana Constantina
%A Manolescu, Mihai
%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 bear-etal-2020-tuemix
%X 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.
%R 10.18653/v1/2020.semeval-1.178
%U https://aclanthology.org/2020.semeval-1.178
%U https://doi.org/10.18653/v1/2020.semeval-1.178
%P 1316-1321

Markdown (Informal)

[TueMix at SemEval-2020 Task 9: Logistic Regression with Linguistic Feature Set](https://aclanthology.org/2020.semeval-1.178) (Bear et al., SemEval 2020)

• TueMix at SemEval-2020 Task 9: Logistic Regression with Linguistic Feature Set (Bear et al., SemEval 2020)

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.