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        <title>Aff2Vec: Affect–Enriched Distributional Word Representations</title>
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    <abstract>Human communication includes information, opinions and reactions. Reactions are often captured by the affective-messages in written as well as verbal communications. While there has been work in affect modeling and to some extent affective content generation, the area of affective word distributions is not well studied. Synsets and lexica capture semantic relationships across words. These models, however, lack in encoding affective or emotional word interpretations. Our proposed model, Aff2Vec, provides a method for enriched word embeddings that are representative of affective interpretations of words. Aff2Vec outperforms the state-of-the-art in intrinsic word-similarity tasks. Further, the use of Aff2Vec representations outperforms baseline embeddings in downstream natural language understanding tasks including sentiment analysis, personality detection, and frustration prediction.</abstract>
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%0 Conference Proceedings
%T Aff2Vec: Affect–Enriched Distributional Word Representations
%A Khosla, Sopan
%A Chhaya, Niyati
%A Chawla, Kushal
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F khosla-etal-2018-aff2vec
%X Human communication includes information, opinions and reactions. Reactions are often captured by the affective-messages in written as well as verbal communications. While there has been work in affect modeling and to some extent affective content generation, the area of affective word distributions is not well studied. Synsets and lexica capture semantic relationships across words. These models, however, lack in encoding affective or emotional word interpretations. Our proposed model, Aff2Vec, provides a method for enriched word embeddings that are representative of affective interpretations of words. Aff2Vec outperforms the state-of-the-art in intrinsic word-similarity tasks. Further, the use of Aff2Vec representations outperforms baseline embeddings in downstream natural language understanding tasks including sentiment analysis, personality detection, and frustration prediction.
%U https://aclanthology.org/C18-1187
%P 2204-2218

Markdown (Informal)

[Aff2Vec: Affect–Enriched Distributional Word Representations](https://aclanthology.org/C18-1187) (Khosla et al., COLING 2018)

• Aff2Vec: Affect–Enriched Distributional Word Representations (Khosla et al., COLING 2018)

ACL

• Sopan Khosla, Niyati Chhaya, and Kushal Chawla. 2018. Aff2Vec: Affect–Enriched Distributional Word Representations. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2204–2218, Santa Fe, New Mexico, USA. Association for Computational Linguistics.