Validiteit van de Nederlandse versie van de Linguistic Inquiry and Word Count (liwc)

By H. Zijlstra, H. Middendorp, Tanja van Meerveld, R. Geenen. 2005

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In Natural Language Processing

Citations in the corpus

CausaLM: Causal Model Explanation Through Counterfactual Language Models (2020, methodology): “Specifically, researchers have been looking into methods of measuring other confounders via text (Pennebaker, Francis, and Booth 2001; Saha et al. 2019), or using text as confounders (Johansson, Shalit, and Sontag 2016; De Choudhury et al. 2016; Roberts, Stewart, and Nielsen 2018).”
"How Old Do You Think I Am?" A Study of Language and Age in Twitter (2013, methodology): “The following variables capture stylistic aspects that a person usually is not aware of: • LIWC-prepositions , the proportion of prepositions such as for, by and on .”
The Lie Detector: Explorations in the Automatic Recognition of Deceptive Language (2009, methodology): “We use the classes of words as defined in the Linguistic Inquiry and Word Count (LIWC), which was developed as a resource for psycholinguistic analysis (Pennebaker and Francis, 1999).”
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020, methodology): “For example, Saha et al. (2019) use categories of the Linguistic Inquiry and Word Count (LIWC) lexicon (Pennebaker et al., 2001) such as tentativeness, inhibition, and negative affect, and use indicators of these categories in the text as confounders.”
Automatic detection of cyberbullying in social media text (2018, background): “For both languages, we included the relative frequency of all 68 psychometric categories in the Linguistic Inquiry and Word Count (LIWC) dictionary for English(Pennebaker et al., 2001) and Dutch (Zijlstra et al., 2004).”
Effects of an Advancing Tenure on CEO Cognitive Complexity (2020, methodology): “…Linguistic Inquiry and Word Count (LIWC) software, which was developed and extensively validated by Pennebaker and colleagues (Duriau et al. 2007, Pennebaker et al. 2007, Pennebaker et al. 2015), and the dictionaries developed by Loughran and McDonald (2011), specifically for analyzing textual…”
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).”
Managing Impressions and Relationships on Facebook (2013, methodology): “After this content was removed, the remaining 1,227 messages were analyzed using the Mac version of LIWC 2007 (Pennebaker et al., 2007).”
Computational Sociolinguistics: A Survey (2016, background): “Review sites, such as TripAdvisor (Michael and Otterbacher 2014), IMDb (Hemphill and Otterbacher 2012) and beer review communities (Danescu-Niculescu-Mizil et al. 2013b), have also been used in studies on language in online communities.”
Divergence of Sentiment and Stock Market Trading (2017, background): “Facebook’s use of LIWC to determine positive and negative sentiment is supported by the academic literature (Pennebaker et al., 2001).”