Dictionary-Based Text Analysis
Dictionary-based text analysis measures concepts in text by counting how often words belonging to predefined category lists — dictionaries — appear in each document. It is the workhorse lexicon method behind tools like LIWC and the General Inquirer, prized for its transparency and scalability: a category score is simply the share of a document's words that match the category's word list.
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Sources
- Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003). Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology, 54, 547–577. DOI: 10.1146/annurev.psych.54.101601.145041 ↗
- Grimmer, J., & Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297. DOI: 10.1093/pan/mps028 ↗
How to cite this page
ScholarGate. (2026, June 22). Dictionary-Based Text Analysis in Communication. ScholarGate. https://scholargate.app/en/communication/dictionary-based-text-analysis-comm
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Automated Content AnalysisCommunication↔ compare
- LIWC Text AnalysisCommunication↔ compare
- Manifest Content AnalysisCommunication↔ compare
- Sentiment Analysis in CommunicationCommunication↔ compare