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Romanian is one of the understudied languages in computational linguistics,with few resources available for the development of natural language processingtools. In this paper, we introduce LaRoSeDa, a Large Romanian Sentiment DataSet, which is composed of 15,000 positive and negative reviews collected fromone of the largest Romanian e-commerce platforms. We employ two sentimentclassification methods as baselines for our new data set, one based onlow-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 withself-organizing maps (SOMs), obtaining better results because the generatedclusters of word embeddings are closer to the Zipf's law distribution, which isknown to govern natural language. We also demonstrate the generalizationcapacity of using SOMs for the clustering of word embeddings on anotherrecently-introduced Romanian data set, for text categorization by topic.

Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set