Frustrated, Polite, or Formal: Quantifying Feelings and Tone in Email

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 (M SE) 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.

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

“…On the other hands, some recent works attempted to provide empirical evidence of style dependencies but in very limited setting: Warriner et al (2013) conducted extensive analysis on emotional norms and their correlation in lexical features of text. Chhaya et al (2018) studied correlation of formality, frustration, and politeness but on small size of samples (i.e., 960 emails). Preoţiuc-Pietro and Ungar (2018) focused on correlation across demographic information (e.g., age, gender, race) and with some other factors such as emotions.…”

Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

“…While we focus on politeness strategies, they are not the only circumstance-sensitive linguistic signals that may be lost or altered during transmission, nor the only type that are subject to individual or culturalspecific perceptions. Other examples commonly observed in communication include, but are not limited to, formality (Rao and Tetreault, 2018) and emotional tones (Chhaya et al, 2018;Raji and de Melo, 2020). As we are provided with more opportunities to interact with people across cultural and language barriers, the risk of misunderstandings in communication also grows (Chang et al, 2020a).…”

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“…For example, in a study of the respectfulness of police language, Voigt et al (2017) found that officers were consistently less likely to use respectful language with black community members than with white community members-a disparity in a positive social dimension. As NLP solutions have been developed for other social dimensions of language such as politeness (Danescu-Niculescu-Mizil et al, 2013;Munkova et al, 2013;Chhaya et al, 2018) and formality (Brooke et al, 2010;Sheikha and Inkpen, 2011;Pavlick and Tetreault, 2016), these methods could be readily adapted for identifying such systematic bias for additional social categories and settings.…”