The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text

• Autor(a) principal: Altszyler, E
• Data de Publicação: 2017
• Outros Autores: Ribeiro, Sidarta Tollendal Gomes, Sigman, M, Fernández Slezak, D
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRN
• Texto Completo: https://repositorio.ufrn.br/jspui/handle/123456789/24164; https://doi.org/10.1016/j.concog.2017.09.004
• topic: Dream content analysis; Word2vec; Latent Semantic Analysis

Computer-based dreams content analysis relies on word frequencies within predefined categories in order to identify different elements in text. As a complementary approach, we explored the capabilities and limitations of word-embedding techniques to identify word usage patterns among dream reports. These tools allow us to quantify words associations in text and to identify the meaning of target words. Word-embeddings have been extensively studied in large datasets, but only a few studies analyze semantic representations in small corpora. To fill this gap, we compared Skip-gram and Latent Semantic Analysis (LSA) capabilities to extract semantic associations from dream reports. LSA showed better performance than Skip-gram in small size corpora in two tests. Furthermore, LSA captured relevant word associations in dream collection, even in cases with low-frequency words or small numbers of dreams. Word associations in dreams reports can thus be quantified by LSA, which opens new avenues for dream interpretation and decoding.