@inproceedings{lyngbaek-etal-2026-sentiment,
title = "Is Sentiment Banana-Shaped? Exploring the Geometry and Portability of Sentiment Concept Vectors",
author = "Lyngbaek, Laurits and
Feldkamp, Pascale and
Bizzoni, Yuri and
Nielbo, Kristoffer and
Enevoldsen, Kenneth",
editor = "Barnes, Jeremy and
Barriere, Valentin and
De Clercq, Orph{\'e}e and
Klinger, Roman and
Nouri, C{\'e}lia and
Nozza, Debora and
Singh, Pranaydeep",
booktitle = "The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis ({WASSA} 2026)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wassa-1.13/",
pages = "146--160",
ISBN = "979-8-89176-378-4",
abstract = "Use cases of sentiment analysis in the humanities often require contextualized, continuous scores. Concept Vector Projections (CVP) offer a recent solution: by modeling sentiment as a direction in embedding space, they produce continuous, multilingual scores that align closely with human judgments. Yet the method{'}s portability across domains and underlying assumptions remain underexplored.We evaluate CVP across genres, historical periods, languages, and affective dimensions, finding that concept vectors trained on one corpus transfer well to others with minimal performance loss. To understand the patterns of generalization, we further examine the linearity assumption underlying CVP. Our findings suggest that while CVP is a portable approach that effectively captures generalizable patterns, its linearity assumption is approximate, pointing to potential for further development. Code available at: github.com/lauritswl/representation-transfer"
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<abstract>Use cases of sentiment analysis in the humanities often require contextualized, continuous scores. Concept Vector Projections (CVP) offer a recent solution: by modeling sentiment as a direction in embedding space, they produce continuous, multilingual scores that align closely with human judgments. Yet the method’s portability across domains and underlying assumptions remain underexplored.We evaluate CVP across genres, historical periods, languages, and affective dimensions, finding that concept vectors trained on one corpus transfer well to others with minimal performance loss. To understand the patterns of generalization, we further examine the linearity assumption underlying CVP. Our findings suggest that while CVP is a portable approach that effectively captures generalizable patterns, its linearity assumption is approximate, pointing to potential for further development. Code available at: github.com/lauritswl/representation-transfer</abstract>
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%0 Conference Proceedings
%T Is Sentiment Banana-Shaped? Exploring the Geometry and Portability of Sentiment Concept Vectors
%A Lyngbaek, Laurits
%A Feldkamp, Pascale
%A Bizzoni, Yuri
%A Nielbo, Kristoffer
%A Enevoldsen, Kenneth
%Y Barnes, Jeremy
%Y Barriere, Valentin
%Y De Clercq, Orphée
%Y Klinger, Roman
%Y Nouri, Célia
%Y Nozza, Debora
%Y Singh, Pranaydeep
%S The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-378-4
%F lyngbaek-etal-2026-sentiment
%X Use cases of sentiment analysis in the humanities often require contextualized, continuous scores. Concept Vector Projections (CVP) offer a recent solution: by modeling sentiment as a direction in embedding space, they produce continuous, multilingual scores that align closely with human judgments. Yet the method’s portability across domains and underlying assumptions remain underexplored.We evaluate CVP across genres, historical periods, languages, and affective dimensions, finding that concept vectors trained on one corpus transfer well to others with minimal performance loss. To understand the patterns of generalization, we further examine the linearity assumption underlying CVP. Our findings suggest that while CVP is a portable approach that effectively captures generalizable patterns, its linearity assumption is approximate, pointing to potential for further development. Code available at: github.com/lauritswl/representation-transfer
%U https://aclanthology.org/2026.wassa-1.13/
%P 146-160
Markdown (Informal)
[Is Sentiment Banana-Shaped? Exploring the Geometry and Portability of Sentiment Concept Vectors](https://aclanthology.org/2026.wassa-1.13/) (Lyngbaek et al., WASSA 2026)
ACL