Detalhes bibliográficos
| Ano de defesa: |
2010 |
| Autor(a) principal: |
Bértoli-Dutra, Patrícia
 |
| Orientador(a): |
Sardinha, Antonio Paulo Berber |
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Tese
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Pontifícia Universidade Católica de São Paulo
|
| Programa de Pós-Graduação: |
Programa de Estudos Pós-Graduados em Linguística Aplicada e Estudos da Linguagem
|
| Departamento: |
Lingüística
|
| País: |
BR
|
| Palavras-chave em Português: |
|
| Área do conhecimento CNPq: |
|
| Link de acesso: |
https://tede2.pucsp.br/handle/handle/14145
|
Resumo: |
This research aims at finding the dimensions of linguistic variation in order to categorize Anglo-American popular music, by observing their lyrics. The main theoretical framework for the research is provided by Corpus Linguistics (BIBER, 1988; SINCLAIR, 1991; BERBER SARDINHA, 2004), which views the language from a probabilistic and functional perspective, assuming that linguistic variation occurs according to the context (FIRTH, 1957; HALLIDAY; HASAN, 1989; HALLIDAY, 1991; HALLIDAY; WEBSTER; 2002). Popular music was considered for its social relevance (MOORE, 2003; STARR; WATERMAN, 2007) and its textual representation, the lyrics, considered as a source for linguistic investigation. Thus, we selected Biber s model for a Multi-dimension analysis, which predicts that texts should be analyzed not only taking into account one but several linguistic features so as to determine their variation across linguistic functions. The corpus collected for the study is consisted of approximately 1,200,000 words from 6,290 song lyrics originally written in English. The corpus was tagged for its parts-of-speech features and for its semantic groupings. These features and the most frequent lexical bundles (3-grams) in the corpus and in general English (Google N-Gram corpus) were considered as variables for the factor extraction at the SPSS program. Factor analysis reduces the huge number of variables, grouping them according to their co-occurrence. This procedure is done through the identification of the distribution patterns of variables. The 97 initial variables in our research were grouped into 13 grammar variables, 8 semantic variables, and 2 pattern variables (3-grams). Factor analysis resulted in three factors for each of those groups. From their interpretation seven different dimensions emerged: argumentative versus informative; argumentative and pattern; interactive versus descriptive; past narratives versus immediate context; personal acts; emotion and society; and musical manifestation. The analysis of song lyrics on the dimensional scale showed how singers and bands, musical styles and the decade of the recordings are closer or more distant to each other in linguistic terms |
