The automated recognition of emotions, moods, and mental disorders is a critical area of research with profound implications for advancing human-computer interaction and digital healthcare. However, the development of robust models is fundamentally dependent on high-quality, well-documented datasets. Researchers often face a complex and fragmented domain, making the selection of appropriate data resources problematic. To address this challenge, this article presents a comprehensive survey of key datasets for the recognition of emotions and mental disorders. We introduce a novel structured taxonomy, which classifies these resources based on crucial dimensions: label structure, signal and modality, collection methodology, domain and purpose, and cultural and idiomatic sensitivity. This framework provides a systematic guide for researchers and practitioners to make more informed decisions in dataset selection. By clarifying the strengths, limitations, and provenance of existing datasets, this work facilitates the development of more effective computational systems, fostering impactful innovation in the field.

Software maintenance plays an important role during software development and life cycle. Indeed, previous works show that maintenance activities consume most of the software budget. Therefore, understanding how these activities are performed can help software managers to previously plan and allocate resources in projects. Despite previous works, there is still a lack in accurate models to classify developers commits into maintenance activities. In the present article, we propose improvements in a state-of-the-art approach used to classify commits. Particularly, we include three additional features in the classification model and we use XGBoost, a boosting tree learning algorithm, for classification. Experimental results show that our approach outperforms the state-of-the-art baseline achieving more than 77% of accuracy and more than 64% in Kappa metric.

A substantial fraction of web search queries contain references to entities, such as persons, organizations, and locations. Recently, methods that exploit named entities have been shown to be more effective for query expansion than traditional pseudorelevance feedback methods. In this article, we introduce a supervised learning approach that exploits named entities for query expansion using Wikipedia as a repository of high‐quality feedback documents. In contrast with existing entity‐oriented pseudorelevance feedback approaches, we tackle query expansion as a learning‐to‐rank problem. As a result, not only do we select effective expansion terms but we also weigh these terms according to their predicted effectiveness. To this end, we exploit the rich structure of Wikipedia articles to devise discriminative term features, including each candidate term's proximity to the original query terms, as well as its frequency across multiple article fields and in category and infobox descriptors. Experiments on three Text REtrieval Conference web test collections attest the effectiveness of our approach, with gains of up to 23.32% in terms of mean average precision, 19.49% in terms of precision at 10, and 7.86% in terms of normalized discounted cumulative gain compared with a state‐of‐the‐art approach for entity‐oriented query expansion.

We here propose a new method for expanding entity related queries that automatically filters, weights and ranks candidate expasion terms extracted from Wikipedia articles related to the original query. Our method is based on state-of-the-art tag recommendation methods that exploit heuristic metrics to estimate the descriptive capacity of a given term. Originally proposed for the context of tags, we here apply these recommendation methods to weight and rank terms extracted from multiple fields of Wikipedia articles according to their relevance for the article. We evaluate our method comparing it against three state-of-the-art baselines in three collections. Our results indicate that our method outperforms all baselines in all collections, with relative gains in MAP of up to 14% against the best ones.