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            <namePart type="given">Alexandra</namePart>
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    <abstract>We investigate the relationship between basic principles of human morality and the expression of opinions in user-generated text data. We assume that people’s backgrounds, culture, and values are associated with their perceptions and expressions of everyday topics, and that people’s language use reflects these perceptions. While personal values and social effects are abstract and complex concepts, they have practical implications and are relevant for a wide range of NLP applications. To extract human values (in this paper, morality) and measure social effects (morality and stance), we empirically evaluate the usage of a morality lexicon that we expanded via a quality controlled, human in the loop process. As a result, we enhanced the Moral Foundations Dictionary in size (from 324 to 4,636 syntactically disambiguated entries) and scope. We used both lexica for feature-based and deep learning classification (SVM, RF, and LSTM) to test their usefulness for measuring social effects. We find that the enhancement of the original lexicon led to measurable improvements in prediction accuracy for the selected NLP tasks.</abstract>
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%0 Conference Proceedings
%T Enhancing the Measurement of Social Effects by Capturing Morality
%A Rezapour, Rezvaneh
%A Shah, Saumil H.
%A Diesner, Jana
%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 rezapour-etal-2019-enhancing
%X We investigate the relationship between basic principles of human morality and the expression of opinions in user-generated text data. We assume that people’s backgrounds, culture, and values are associated with their perceptions and expressions of everyday topics, and that people’s language use reflects these perceptions. While personal values and social effects are abstract and complex concepts, they have practical implications and are relevant for a wide range of NLP applications. To extract human values (in this paper, morality) and measure social effects (morality and stance), we empirically evaluate the usage of a morality lexicon that we expanded via a quality controlled, human in the loop process. As a result, we enhanced the Moral Foundations Dictionary in size (from 324 to 4,636 syntactically disambiguated entries) and scope. We used both lexica for feature-based and deep learning classification (SVM, RF, and LSTM) to test their usefulness for measuring social effects. We find that the enhancement of the original lexicon led to measurable improvements in prediction accuracy for the selected NLP tasks.
%R 10.18653/v1/W19-1305
%U https://aclanthology.org/W19-1305
%U https://doi.org/10.18653/v1/W19-1305
%P 35-45

Markdown (Informal)

[Enhancing the Measurement of Social Effects by Capturing Morality](https://aclanthology.org/W19-1305) (Rezapour et al., WASSA 2019)

• Enhancing the Measurement of Social Effects by Capturing Morality (Rezapour et al., WASSA 2019)

ACL

• Rezvaneh Rezapour, Saumil H. Shah, and Jana Diesner. 2019. Enhancing the Measurement of Social Effects by Capturing Morality. In Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 35–45, Minneapolis, USA. Association for Computational Linguistics.