Psychological aspects of natural language. use: our words, our selves.

By James W. Pennebaker, Matthias R. Mehl, Kate Niederhoffer. 2003

2,682 citations recorded by the source corpus.

Subfields: Clinical Psychology, Experimental and Cognitive Psychology, Social Psychology

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In Clinical Psychology

In Experimental and Cognitive Psychology

In Social Psychology

Citations in the corpus

Getting Gender Right in Neural Machine Translation (2019, background): “) also influence our use of language in terms of word choices or even on the level of syntactic constructions (Tannen, 1991; Pennebaker et al., 2003).”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Even if such words might not be useful for certain analyses, for other applications they may embed useful signals about e.g., authorship and/or emotional states (Pennebaker et al., 2003; Saif et al., 2014), threatening as a result the research validity (Denny and Spirling, 2016).”
Language Style Matching as a Predictor of Social Dynamics in Small Groups (2010, background): “Second, function words, which are the underlying features of the LSM metric, are effective measures of mimicry because they are frequently used, nonconsciously produced, and context independent (Chung & Pennebaker, 2007; Pennebaker et al., 2003).”
Learning Multimodal Gender Profile using Neural Networks (2017, background): “Pennebaker et al. (2003) made some initial approaches to asses how the variation of linguistic characteristics in a text can help in the ﬁnding of gender and age of an author with respect to others.”
Overview of the 3rd Author Profiling Task at PAN 2015 (2015, background): “Pennebaker’s [54] investigated how the style of writing is associated with personal attributes such as age and gender.”
Text Preprocessing for Text Mining in Organizational Research: Review and Recommendations (2020, background): “Function words represent how language is delivered and tend to be used more consistently across situations within individuals (Pennebaker et al., 2003).”
Contextual semantics for sentiment analysis of Twitter (2016, methodology): “…of using training data, lexicon-based methods rely on sentiment lexicons, i.e. pre-built dictionaries of words with associated sentiment orientations such as SentiWordNet (Baccianella et al., 2010), MPQA subjectivity lexicon (Wilson et al., 2005), or the LIWC lexicon (Pennebaker et al., 2003).”
Managing Impressions and Relationships on Facebook (2013, background): “Although the limitation in terms of generalizability is balanced by the advantages of studying language use in naturally occurring environments (Pennebaker et al., 2003), future research needs to examine how these findings generalize to college students in other institutions as well as to other Face”
Emphasizing personal information for Author Profiling: New approaches for term selection and weighting (2018, background): “Works from psychology have studied how language is shared by people [34,35] , and they have established a relation between language usage and personality traits [36,37] and gender differences [38] among others.”