# Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database

arXiv: https://arxiv.org/abs/1610.01520 · HTML: https://mlsanity.com/paper/1610.01520/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2016.

## Built on by (most cited first, 7 of 7)

- [Automatic Scoring of Dream Reports' Emotional Content with Large Language Models](https://mlsanity.com/paper/2302.14828/) (2023)
- [Sequence-to-Sequence Language Models for Character and Emotion Detection in Dream Narratives](https://mlsanity.com/paper/2403.15486/) (2024)
- [Investigating the Frequency Distortion of Word Embeddings and Its Impact on Bias Metrics](https://mlsanity.com/paper/2211.08203/) (2022)
- [n-stage Latent Dirichlet Allocation: A Novel Approach for LDA](https://mlsanity.com/paper/2110.08591/) (2021)
- [Dreams Are More "Predictable'' Than You Think](https://mlsanity.com/paper/2305.05054/) (2023)
- [Normalisation of SWIFT Message Counterparties with Feature Extraction and Clustering](https://mlsanity.com/paper/2508.21081/) (2025)
- [Formalizing Style in Personal Narratives](https://mlsanity.com/paper/2510.08649/) (2025)

## Abstract (arXiv)

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