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

TL;DR

This paper introduces LaRoSeDa, a large Romanian sentiment dataset, and explores clustering word embeddings with self-organizing maps, showing improved results over k-means and demonstrating generalization to text categorization tasks.

Contribution

The paper presents a new Romanian sentiment dataset and innovatively applies self-organizing maps for clustering word embeddings, outperforming k-means and generalizing to text categorization.

Findings

SOMs produce clusters closer to Zipf's law distribution.

SOM-based clustering improves sentiment classification accuracy.

Method generalizes well to Romanian text categorization.

Abstract

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.