Linguistic Inquiry and Word Count (LIWC2007)

By James W. Pennebaker, R. Booth, Martha E. Francis. 2007

2,801 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Challenges in Building Intelligent Open-domain Dialog Systems (2019, methodology): “At each decoding position, the model estimates an affective vector of the already generated prefix by keyword spotting using the Linguistic Inquiry and Word Count (LIWC) dictionary [100].”
Rapid assessment of disaster damage using social media activity (2016, methodology): “…Linguistic Inquiry and Word Count (LIWC), which is a frequency-based tool with unweighted lexicon that is widely used in psychological research ( 72 ), and SentiStrength ( 73 ), which uses a weighted lexicon and takes into account certain features that are prevalent in short messages…”
Predicting Personality from Twitter (2011, background): “The first is that Linguistic Inquiry and Word Count (LIWC) tool [35].”
To nostalgize: mixing memory with affect and desire (2015, methodology): “We subjected the narratives from the ERT (nostalgic vs. ordinary event) to LIWC analyses.”
Executive Extraversion: Career and Firm Outcomes (2018, background): “…is the extraversion score of the individual, and the explanatory variables are word categories from the Linguistic Inquiry and Word Count (LIWC) (Pennebaker et al. 2001) and Machine Readable Cataloguing (MRC) linguistic databases (Coltheart 1981).8 Mairesse et al. (2007) find that the following…”
Affect-LM: A Neural Language Model for Customizable Affective Text Generation (2017, methodology): “Introduced by Pennebaker et al. (2001), LIWC is based on a dictionary, where each word is assigned to a pre-deﬁned LIWC category.”
Developing Age and Gender Predictive Lexica over Social Media (2014, methodology): “The LIWC lexicon, derived manually based on psychological theory, (Pennebaker et al., 2001) had 1136 citations in 2013 alone.”
Where is Your Evidence: Improving Fact-checking by Justification Modeling (2018, methodology): “…and positivity of a statement on a scale of 1-to-5 (Thelwall et al., 2010); emotion using the NRC Emotion Lexicon (EmoLex), which associates each word with eight basic emotions (Mohammad and Turney, 2010), and the Linguistic Inquiry and Word Count (LIWC) lexicon (Pennebaker et al., 2001).”
VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text (2014, background): “…5 http://sentiwordnet.isti.cnr.it/ 6 http://sentic.net/ LIWC is well-established and has been both internally and externally validated in a process spanning more than a decade of work by psychologists, sociologists, and linguists (Pennebaker et al., 2001; Pennebaker et al., 2007).”
From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses (2015, methodology): “We examine differences in usage patterns of categories from the Linguistic Inquiry Word Count (LIWC), a widely used psychometrically validated tool for psychology-related analysis of language (Pennebaker et al., 2007; Pennebaker et al., 2001).”
Online Review Helpfulness: Role of Qualitative Factors (2016, background): “LIWC has been proven for its highly reliable convergent validity of emotions as coded by software and that coded by human coders (Pennebaker et al. 2007).”
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking (2017, background): “First among these lexicons is the Linguistic Inquiry and Word Count (LIWC), a lexicon widely used in social science studies (Pennebaker et al., 2015).”