Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa -- A Large Romanian Sentiment Data Set
This work provides a new dataset and an improved method for sentiment analysis in Romanian, a language with limited NLP resources, benefiting researchers and developers working on Romanian language processing.
This paper introduces LaRoSeDa, a new Romanian sentiment dataset of 15,000 positive and negative reviews. The authors demonstrate that using Self-Organizing Maps (SOMs) to cluster word embeddings for sentiment classification yields better results than k-means, achieving clusters that better align with Zipf's law.
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.