  Romanian is one of the understudied languages in computational linguistics,
with few resources available for the development of natural language processing
tools. In this paper, we introduce LaRoSeDa, a Large Romanian Sentiment Data
Set, which is composed of 15,000 positive and negative reviews collected from
one of the largest Romanian e-commerce platforms. We employ two sentiment
classification methods as baselines for our new data set, one based on
low-level features (character n-grams) and one based on high-level features
(bag-of-word-embeddings generated by clustering word embeddings with k-means).
As an additional contribution, we replace the k-means clustering algorithm with
self-organizing maps (SOMs), obtaining better results because the generated
clusters of word embeddings are closer to the Zipf's law distribution, which is
known to govern natural language. We also demonstrate the generalization
capacity of using SOMs for the clustering of word embeddings on another
recently-introduced Romanian data set, for text categorization by topic.
