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        <title>Sentiment Analysis Is Not Solved! Assessing and Probing Sentiment Classification</title>
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        <namePart type="given">Jeremy</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP</title>
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        <name type="personal">
            <namePart type="given">Tal</namePart>
            <namePart type="family">Linzen</namePart>
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        <name type="personal">
            <namePart type="given">Yonatan</namePart>
            <namePart type="family">Belinkov</namePart>
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    <abstract>Neural methods for sentiment analysis have led to quantitative improvements over previous approaches, but these advances are not always accompanied with a thorough analysis of the qualitative differences. Therefore, it is not clear what outstanding conceptual challenges for sentiment analysis remain. In this work, we attempt to discover what challenges still prove a problem for sentiment classifiers for English and to provide a challenging dataset. We collect the subset of sentences that an (oracle) ensemble of state-of-the-art sentiment classifiers misclassify and then annotate them for 18 linguistic and paralinguistic phenomena, such as negation, sarcasm, modality, etc. Finally, we provide a case study that demonstrates the usefulness of the dataset to probe the performance of a given sentiment classifier with respect to linguistic phenomena.</abstract>
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%0 Conference Proceedings
%T Sentiment Analysis Is Not Solved! Assessing and Probing Sentiment Classification
%A Barnes, Jeremy
%A Øvrelid, Lilja
%A Velldal, Erik
%Y Linzen, Tal
%Y Chrupała, Grzegorz
%Y Belinkov, Yonatan
%Y Hupkes, Dieuwke
%S Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F barnes-etal-2019-sentiment
%X Neural methods for sentiment analysis have led to quantitative improvements over previous approaches, but these advances are not always accompanied with a thorough analysis of the qualitative differences. Therefore, it is not clear what outstanding conceptual challenges for sentiment analysis remain. In this work, we attempt to discover what challenges still prove a problem for sentiment classifiers for English and to provide a challenging dataset. We collect the subset of sentences that an (oracle) ensemble of state-of-the-art sentiment classifiers misclassify and then annotate them for 18 linguistic and paralinguistic phenomena, such as negation, sarcasm, modality, etc. Finally, we provide a case study that demonstrates the usefulness of the dataset to probe the performance of a given sentiment classifier with respect to linguistic phenomena.
%R 10.18653/v1/W19-4802
%U https://aclanthology.org/W19-4802
%U https://doi.org/10.18653/v1/W19-4802
%P 12-23

Markdown (Informal)

[Sentiment Analysis Is Not Solved! Assessing and Probing Sentiment Classification](https://aclanthology.org/W19-4802) (Barnes et al., BlackboxNLP 2019)

• Sentiment Analysis Is Not Solved! Assessing and Probing Sentiment Classification (Barnes et al., BlackboxNLP 2019)

ACL

• Jeremy Barnes, Lilja Øvrelid, and Erik Velldal. 2019. Sentiment Analysis Is Not Solved! Assessing and Probing Sentiment Classification. In Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pages 12–23, Florence, Italy. Association for Computational Linguistics.