Connotation is a dimension of lexical meaning at the semantic-pragmatic interface. Connotations can be used to express point of views, perspectives, and implied emotional associations. Variations in connotations of the same lexical item can occur at different level of analysis: from individuals, to community of speech, specific domains, and even time. In this paper, we present a simple yet effective method to assign connotative values to selected target items and to quantify connotation shifts. We test our method via a set of experiments using different social media data (Reddit and Twitter) and languages (English and Italian). While we kept the connotative axis (i.e., the polarity associated to a lexical item) fixed, we investigated connotation shifts along two dimensions: the first target shifts across communities of speech and domain while the second targets shifts in time. Our results indicate the validity of the proposed method and its potential application for the identification of connotation shifts and application to automatically induce specific connotation lexica.

Automatically Computing Connotative Shifts of Lexical Items

Basile V.;Patti V.
2022-01-01

Abstract

Connotation is a dimension of lexical meaning at the semantic-pragmatic interface. Connotations can be used to express point of views, perspectives, and implied emotional associations. Variations in connotations of the same lexical item can occur at different level of analysis: from individuals, to community of speech, specific domains, and even time. In this paper, we present a simple yet effective method to assign connotative values to selected target items and to quantify connotation shifts. We test our method via a set of experiments using different social media data (Reddit and Twitter) and languages (English and Italian). While we kept the connotative axis (i.e., the polarity associated to a lexical item) fixed, we investigated connotation shifts along two dimensions: the first target shifts across communities of speech and domain while the second targets shifts in time. Our results indicate the validity of the proposed method and its potential application for the identification of connotation shifts and application to automatically induce specific connotation lexica.
2022
27th International Conference on Applications of Natural Language to Information Systems, NLDB 2022
Valencia, Spain
2022
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Springer Science and Business Media Deutschland GmbH
13286
425
436
978-3-031-08472-0
978-3-031-08473-7
https://link.springer.com/chapter/10.1007/978-3-031-08473-7_39
Connotative shift; Social media; Word embeddings
Basile V.; Caselli T.; Koufakou A.; Patti V.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1886902
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