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    <titleInfo>
        <title>RED: A Novel Dataset for Romanian Emotion Detection from Tweets</title>
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    <name type="personal">
        <namePart type="given">Alexandra</namePart>
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    <name type="personal">
        <namePart type="given">Liviu</namePart>
        <namePart type="given">P</namePart>
        <namePart type="family">Dinu</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>In Romanian language there are some resources for automatic text comprehension, but for Emotion Detection, not lexicon-based, there are none. To cover this gap, we extracted data from Twitter and created the first dataset containing tweets annotated with five types of emotions: joy, fear, sadness, anger and neutral, with the intent of being used for opinion mining and analysis tasks. In this article we present some features of our novel dataset, and create a benchmark to achieve the first supervised machine learning model for automatic Emotion Detection in Romanian short texts. We investigate the performance of four classical machine learning models: Multinomial Naive Bayes, Logistic Regression, Support Vector Classification and Linear Support Vector Classification. We also investigate more modern approaches like fastText, which makes use of subword information. Lastly, we fine-tune the Romanian BERT for text classification and our experiments show that the BERT-based model has the best performance for the task of Emotion Detection from Romanian tweets. Keywords: Emotion Detection, Twitter, Romanian, Supervised Machine Learning</abstract>
    <identifier type="citekey">ciobotaru-dinu-2021-red</identifier>
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        <url>https://aclanthology.org/2021.ranlp-1.34</url>
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        <date>2021-09</date>
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%0 Conference Proceedings
%T RED: A Novel Dataset for Romanian Emotion Detection from Tweets
%A Ciobotaru, Alexandra
%A Dinu, Liviu P.
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F ciobotaru-dinu-2021-red
%X In Romanian language there are some resources for automatic text comprehension, but for Emotion Detection, not lexicon-based, there are none. To cover this gap, we extracted data from Twitter and created the first dataset containing tweets annotated with five types of emotions: joy, fear, sadness, anger and neutral, with the intent of being used for opinion mining and analysis tasks. In this article we present some features of our novel dataset, and create a benchmark to achieve the first supervised machine learning model for automatic Emotion Detection in Romanian short texts. We investigate the performance of four classical machine learning models: Multinomial Naive Bayes, Logistic Regression, Support Vector Classification and Linear Support Vector Classification. We also investigate more modern approaches like fastText, which makes use of subword information. Lastly, we fine-tune the Romanian BERT for text classification and our experiments show that the BERT-based model has the best performance for the task of Emotion Detection from Romanian tweets. Keywords: Emotion Detection, Twitter, Romanian, Supervised Machine Learning
%U https://aclanthology.org/2021.ranlp-1.34
%P 291-300

Markdown (Informal)

[RED: A Novel Dataset for Romanian Emotion Detection from Tweets](https://aclanthology.org/2021.ranlp-1.34) (Ciobotaru & Dinu, RANLP 2021)

• RED: A Novel Dataset for Romanian Emotion Detection from Tweets (Ciobotaru & Dinu, RANLP 2021)

ACL

• Alexandra Ciobotaru and Liviu P. Dinu. 2021. RED: A Novel Dataset for Romanian Emotion Detection from Tweets. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 291–300, Held Online. INCOMA Ltd..