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Exploring fine-tuned embeddings that model intensifiers for emotion analysis
Bostan, Laura; Klinger, Roman (2024): Exploring fine-tuned embeddings that model intensifiers for emotion analysis, in: Bamberg: Otto-Friedrich-Universität, S. 25–34.
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Year of publication:
2024
Pages:
Source/Other editions:
Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis / Alexandra Balahur, Roman Klinger, Veronique Hoste, Carlo Strapparava, Orphee De Clercq (Hg.). - Minneapolis : Association for Computational Linguistics, 2019 S. 25–34.
Year of first publication:
2019
Language:
English
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.
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Computerlinguistik
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Keywords:
fine-tuned embeddings
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Peer Reviewed:
Yes:
International Distribution:
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Open Access Journal:
Yes:
Type:
Conferenceobject
Activation date:
August 19, 2024
Permalink
https://fis.uni-bamberg.de/handle/uniba/95816