The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text.
Edgar Altszyler, Sidarta Ribeiro, Mariano Sigman, Diego Fernández Slezak
Consciousness and Cognition November 1, 2017 DOI: 10.1016/j.concog.2017.09.004 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Comparison of computational methods Peer reviewed |
|---|---|
| Keywords | Dream content analysis Latent semantic analysis Word2vec |
| Key points | Latent Semantic Analysis outperformed Skip-gram in extracting semantic associations from small dream report corpora and captured relevant word associations even with low-frequency words or few dreams. |
Abstract
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.