  Word embeddings have been extensively studied in large text datasets.
However, only a few studies analyze semantic representations of small corpora,
particularly relevant in single-person text production studies. In the present
paper, we compare Skip-gram and LSA capabilities in this scenario, and we test
both techniques to extract relevant semantic patterns in single-series dreams
reports. LSA showed better performance than Skip-gram in small size training
corpus in two semantic tests. As a study case, we show that LSA can capture
relevant words associations in dream reports series, even in cases of small
number of dreams or low-frequency words. We propose that LSA can be used to
explore words associations in dreams reports, which could bring new insight
into this classic research area of psychology
