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.

本研究介绍了LaRoSeDa，一个由15000个正面和负面评论组成的大型罗马尼亚情感数据集，提出了两种基线情感分类方法，并使用自组织映射算法改进了聚类效果，并且成功应用于罗马尼亚文本分类。

使用自组织映射对单词嵌入进行聚类——在LaRoSeDa一大型罗马尼亚情感数据集上的应用