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        <title>Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis</title>
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    <abstract>Adjective phrases like “a little bit surprised”, “completely shocked”, or “not stunned at all” are not handled properly by current state-of-the-art emotion classification and intensity prediction systems. Based on this finding, we analyze differences between embeddings used by these systems in regard to their capability of handling such cases and argue that intensifiers in context of emotion words need special treatment, as is established for sentiment polarity classification, but not for more fine-grained emotion prediction. To resolve this issue, we analyze different aspects of a post-processing pipeline which enriches the word representations of such phrases. This includes expansion of semantic spaces at the phrase level and sub-word level followed by retrofitting to emotion lexicons. We evaluate the impact of these steps with ‘A La Carte and Bag-of-Substrings extensions based on pretrained GloVe,Word2vec, and fastText embeddings against a crowd-sourced corpus of intensity annotations for tweets containing our focus phrases. We show that the fastText-based models do not gain from handling these specific phrases under inspection. For Word2vec embeddings, we show that our post-processing pipeline improves the results by up to 8% on a novel dataset densly populated with intensifiers while it does not decrease the performance on the established EmoInt dataset.</abstract>
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%0 Conference Proceedings
%T Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis
%A Bostan, Laura Ana Maria
%A Klinger, Roman
%Y Balahur, Alexandra
%Y Klinger, Roman
%Y Hoste, Veronique
%Y Strapparava, Carlo
%Y De Clercq, Orphee
%S Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, USA
%F bostan-klinger-2019-exploring
%X Adjective phrases like “a little bit surprised”, “completely shocked”, or “not stunned at all” are not handled properly by current state-of-the-art emotion classification and intensity prediction systems. Based on this finding, we analyze differences between embeddings used by these systems in regard to their capability of handling such cases and argue that intensifiers in context of emotion words need special treatment, as is established for sentiment polarity classification, but not for more fine-grained emotion prediction. To resolve this issue, we analyze different aspects of a post-processing pipeline which enriches the word representations of such phrases. This includes expansion of semantic spaces at the phrase level and sub-word level followed by retrofitting to emotion lexicons. We evaluate the impact of these steps with ‘A La Carte and Bag-of-Substrings extensions based on pretrained GloVe,Word2vec, and fastText embeddings against a crowd-sourced corpus of intensity annotations for tweets containing our focus phrases. We show that the fastText-based models do not gain from handling these specific phrases under inspection. For Word2vec embeddings, we show that our post-processing pipeline improves the results by up to 8% on a novel dataset densly populated with intensifiers while it does not decrease the performance on the established EmoInt dataset.
%R 10.18653/v1/W19-1304
%U https://aclanthology.org/W19-1304
%U https://doi.org/10.18653/v1/W19-1304
%P 25-34

Markdown (Informal)

[Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis](https://aclanthology.org/W19-1304) (Bostan & Klinger, WASSA 2019)

• Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis (Bostan & Klinger, WASSA 2019)

ACL

• Laura Ana Maria Bostan and Roman Klinger. 2019. Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis. In Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 25–34, Minneapolis, USA. Association for Computational Linguistics.