@inproceedings{chhaya-etal-2018-frustrated,
title = "Frustrated, Polite, or Formal: Quantifying Feelings and Tone in Email",
author = "Chhaya, Niyati and
Chawla, Kushal and
Goyal, Tanya and
Chanda, Projjal and
Singh, Jaya",
editor = "Nissim, Malvina and
Patti, Viviana and
Plank, Barbara and
Wagner, Claudia",
booktitle = "Proceedings of the Second Workshop on Computational Modeling of People{'}s Opinions, Personality, and Emotions in Social Media",
month = jun,
year = "2018",
address = "New Orleans, Louisiana, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-1111",
doi = "10.18653/v1/W18-1111",
pages = "76--86",
abstract = "Email conversations are the primary mode of communication in enterprises. The email content expresses an individual{'}s needs, requirements and intentions. Affective information in the email text can be used to get an insight into the sender{'}s mood or emotion. We present a novel approach to model human frustration in text. We identify linguistic features that influence human perception of frustration and model it as a supervised learning task. The paper provides a detailed comparison across traditional regression and word distribution-based models. We report a mean-squared error (MSE) of 0.018 against human-annotated frustration for the best performing model. The approach establishes the importance of affect features in frustration prediction for email data. We further evaluate the efficacy of the proposed feature set and model in predicting other tone or affects in text, namely formality and politeness; results demonstrate a comparable performance against the state-of-the-art baselines.",
}
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%0 Conference Proceedings
%T Frustrated, Polite, or Formal: Quantifying Feelings and Tone in Email
%A Chhaya, Niyati
%A Chawla, Kushal
%A Goyal, Tanya
%A Chanda, Projjal
%A Singh, Jaya
%Y Nissim, Malvina
%Y Patti, Viviana
%Y Plank, Barbara
%Y Wagner, Claudia
%S Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana, USA
%F chhaya-etal-2018-frustrated
%X Email conversations are the primary mode of communication in enterprises. The email content expresses an individual’s needs, requirements and intentions. Affective information in the email text can be used to get an insight into the sender’s mood or emotion. We present a novel approach to model human frustration in text. We identify linguistic features that influence human perception of frustration and model it as a supervised learning task. The paper provides a detailed comparison across traditional regression and word distribution-based models. We report a mean-squared error (MSE) of 0.018 against human-annotated frustration for the best performing model. The approach establishes the importance of affect features in frustration prediction for email data. We further evaluate the efficacy of the proposed feature set and model in predicting other tone or affects in text, namely formality and politeness; results demonstrate a comparable performance against the state-of-the-art baselines.
%R 10.18653/v1/W18-1111
%U https://aclanthology.org/W18-1111
%U https://doi.org/10.18653/v1/W18-1111
%P 76-86
Markdown (Informal)
[Frustrated, Polite, or Formal: Quantifying Feelings and Tone in Email](https://aclanthology.org/W18-1111) (Chhaya et al., PEOPLES 2018)
ACL
- Niyati Chhaya, Kushal Chawla, Tanya Goyal, Projjal Chanda, and Jaya Singh. 2018. Frustrated, Polite, or Formal: Quantifying Feelings and Tone in Email. In Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, pages 76–86, New Orleans, Louisiana, USA. Association for Computational Linguistics.