@inproceedings{mulki-etal-2018-tw,
title = "Tw-{S}t{AR} at {S}em{E}val-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification",
author = "Mulki, Hala and
Bechikh Ali, Chedi and
Haddad, Hatem and
Babao{\u{g}}lu, Ismail",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1024",
doi = "10.18653/v1/S18-1024",
pages = "167--171",
abstract = "In this paper, we describe our contribution in SemEval-2018 contest. We tackled task 1 {``}Affect in Tweets{''}, subtask E-c {``}Detecting Emotions (multi-label classification){''}. A multilabel classification system Tw-StAR was developed to recognize the emotions embedded in Arabic, English and Spanish tweets. To handle the multi-label classification problem via traditional classifiers, we employed the binary relevance transformation strategy while a TF-IDF scheme was used to generate the tweets{'} features. We investigated using single and combinations of several preprocessing tasks to further improve the performance. The results showed that specific combinations of preprocessing tasks could significantly improve the evaluation measures. This has been later emphasized by the official results as our system ranked 3rd for both Arabic and Spanish datasets and 14th for the English dataset.",
}
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<abstract>In this paper, we describe our contribution in SemEval-2018 contest. We tackled task 1 “Affect in Tweets”, subtask E-c “Detecting Emotions (multi-label classification)”. A multilabel classification system Tw-StAR was developed to recognize the emotions embedded in Arabic, English and Spanish tweets. To handle the multi-label classification problem via traditional classifiers, we employed the binary relevance transformation strategy while a TF-IDF scheme was used to generate the tweets’ features. We investigated using single and combinations of several preprocessing tasks to further improve the performance. The results showed that specific combinations of preprocessing tasks could significantly improve the evaluation measures. This has been later emphasized by the official results as our system ranked 3rd for both Arabic and Spanish datasets and 14th for the English dataset.</abstract>
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%0 Conference Proceedings
%T Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification
%A Mulki, Hala
%A Bechikh Ali, Chedi
%A Haddad, Hatem
%A Babaoğlu, Ismail
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F mulki-etal-2018-tw
%X In this paper, we describe our contribution in SemEval-2018 contest. We tackled task 1 “Affect in Tweets”, subtask E-c “Detecting Emotions (multi-label classification)”. A multilabel classification system Tw-StAR was developed to recognize the emotions embedded in Arabic, English and Spanish tweets. To handle the multi-label classification problem via traditional classifiers, we employed the binary relevance transformation strategy while a TF-IDF scheme was used to generate the tweets’ features. We investigated using single and combinations of several preprocessing tasks to further improve the performance. The results showed that specific combinations of preprocessing tasks could significantly improve the evaluation measures. This has been later emphasized by the official results as our system ranked 3rd for both Arabic and Spanish datasets and 14th for the English dataset.
%R 10.18653/v1/S18-1024
%U https://aclanthology.org/S18-1024
%U https://doi.org/10.18653/v1/S18-1024
%P 167-171
Markdown (Informal)
[Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification](https://aclanthology.org/S18-1024) (Mulki et al., SemEval 2018)
ACL