NECOS: An annotated corpus to identify constructive news comments in Spanish

Pilar López-Úbeda, Flor Miriam Plaza-del-Arco, Manuel Carlos Díaz-Galiano, M. Teresa Martín-Valdivia


In this paper, we present the NEws and COmments in Spanish (NECOS) corpus, a collection of Spanish comments posted in response to newspaper articles. Following a robust annotation scheme, three annotators labeled the comments as constructive and non-constructive. The articles were published in the newspaper El Mundo between April 3rd and April 30th, 2018. The corpus is composed of a total of 10 news articles and 1,419 comments. Three annotators manually labeled NECOS with an average Cohen’s kappa of 78.97. Our current focus is the study of con-structiveness and the evaluation of the Spanish NECOS corpus. In order to address this goal, we propose a benchmark testing different machine learning systems based on Natural Language Processing: a traditional system and the novel Transformer-based models. Specifically, we compare multilingual models with a monolingual model trained on Spanish in order to highlight the need to create resources trained on a specific language. The monolingual model fine-tuning on NECOS obtain the best result by achieving a macro-average F1 score of 77.24%.

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