2009
DOI: 10.1177/0261927x09351676
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The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods

Abstract: We are in the midst of a technological revolution whereby, for the first time, researchers can link daily word use to a broad array of real-world behaviors. This article reviews several computerized text analysis methods and describes how Linguistic Inquiry and Word Count (LIWC) was created and validated. LIWC is a transparent text analysis program that counts words in psychologically meaningful categories. Empirical results using LIWC demonstrate its ability to detect meaning in a wide variety of experimental… Show more

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Cited by 4,521 publications
(3,715 citation statements)
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References 49 publications
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“…Several have been linked to deception, although the construct may have been operationalized in different ways. According to the meta-analysis, "across the studies when participants were lying they used more negative emotion, more motion words (e.g., arrive, car, go), fewer exclusion words, and less first-person singular" (Tausczik and Pennebaker, 2010). The studies also show that liars use a higher word count, less first-person singular voice, and more "sense" words.…”
Section: Resultsmentioning
confidence: 99%
“…Several have been linked to deception, although the construct may have been operationalized in different ways. According to the meta-analysis, "across the studies when participants were lying they used more negative emotion, more motion words (e.g., arrive, car, go), fewer exclusion words, and less first-person singular" (Tausczik and Pennebaker, 2010). The studies also show that liars use a higher word count, less first-person singular voice, and more "sense" words.…”
Section: Resultsmentioning
confidence: 99%
“…Assume que as palavras usadas pelos indivíduos transmitem informação independente do contexto semântico e que localizar a palavra no discurso permite extrair o seu mundo de valores e significados. A análise decorreu evitando-se palavras repetidas menos de 3 vezes no discurso por participante (Tausczik & Pennebaker, 2010); eliminamos as palavras de função (que compõem frases, incluindo preposições, pronomes, artigos, conjunções); selecionamos palavras mais mencionadas em cada entrevista, e que revelassem conteúdo (transmitem o conteúdo da mensagem, incluindo nomes, verbos regulares, adjetivos e advérbios). Deste modo, emergiram 20 palavras.…”
Section: Procedimento De Análiseunclassified
“…Support for this idea is provided by studies showing that health improvements can be predicted by automated linguistic analysis, e.g. the number of emotion words people use may be indications about their emotional states, thought processes, motivations, and intentions (Tausczik & Pennebaker, 2009). Examples of other predicting word patterns are (1) an increasing use of cognitive or insight words over the days of writing (Pennebaker, Mayne, & Francis, 1997), (2) shifts in the use of pronouns from day to day (Campbell & Pennebaker, 2003), (3) higher use of positive relative to negative emotion words (Pennebaker et al, 1997).…”
Section: Content Analysis Of Narrativesmentioning
confidence: 99%