Comparar métodos

	Dictionary-Based Text Analysis	Sentiment Analysis in Communication
Área	Communication	Communication
Família	Process / pipeline	Process / pipeline
Ano de origem	2003	2010
Autor original	Lexicon tradition (Pennebaker LIWC; General Inquirer)	Adapted into communication research from NLP / opinion mining
Tipo	Word-count text measurement against predefined category dictionaries	Automated classification of message valence/tone
Fonte seminal	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.	Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54.
Outros nomes	Lexicon-based text analysis, Word-count text analysis, Dictionary method for content analysis, Sözlük Tabanlı Metin Analizi	Opinion mining in communication, Tone analysis, Media sentiment analysis, İletişimde Duygu Analizi
Relacionados	4	5
Resumo	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.	Sentiment analysis is the automated estimation of the valence — positive, negative, or neutral tone — of communication messages, adapted from natural-language processing into a core measurement technique for media and communication research. It lets scholars quantify the tone of news coverage, the affect of social-media discourse, or audience reactions across corpora far too large for hand coding, while treating tone as a measurable, validatable construct.