Complaint processing is of great importance for companies because it allows them to understand customer satisfaction levels, which is crucial for business success. It allows them to show the real perceptions of users and thus visualize the problems, which are regularly processed from oral or written natural language, derived from the provision of a service. In addition, the treatment of complaints is relevant because according to the laws of each country, companies have the obligation to respond to these complaints in a specified time. The specialized literature mentions that enterprises lost USD 75 billion due to poor customer service, highlighting that companies need to know and understand customer perceptions, especially emotions, and product reviews to gain insight and learn about customer feedback because of the importance of the voice of the customer for an organization. In general, it is evident that there is a need for research related to computational language processing to handle user requests. The authors show great interest in computational techniques for the processing of this information in natural language and how this could contribute to the improvement of processes within the productive sector. This work searches in indexed journals for information related to computational methods for processing relevant data from user complaints. It is proposed to apply a systematic literature review (SLR) method combining literature review guides by Kitchenham and the PRISMA statement. The systematic process allows the extraction of consistent information, and after applying it, 27 articles were obtained from which the analysis was conducted. The results show various proposals using linguistic, statistical, machine learning, and hybrid methods. We find that most authors combine Natural Language Processing (NLP) and Machine Learning (ML) to create hybrid methods. The methods extract relevant information from complaints of the customers in natural language in various domains, such as government, medical, banks, e-commerce, public services, agriculture, customer service, environmental, and tourism, among others. This work contributes as support for the creation of new systems that can give companies a significant competitive advantage due to their ability to reduce the response time of the complaints as established by law.

The timely detection of suicidal ideation is essential for reducing suicide-related deaths. Early signals of suicidal thoughts can often be identified through the analysis of textual content shared on social media platforms. In this context, Artificial Intelligence (AI) techniques, particularly Natural Language Processing (NLP), have shown strong potential for identifying linguistic patterns that enable the accurate classification of suicide-related content. However, NLP models developed for one language cannot be directly transferred to other languages due to their specific linguistic characteristics, highlighting the need for language-specific approaches. This study proposes a computational model for detecting suicidal ideation in Spanish-language social media posts. The proposed architecture combines Transformer-based representations with BiLSTM and BiTCN neural network layers to capture contextual and sequential linguistic patterns. The model is trained and evaluated using two corpora of different sizes composed of suicide-related posts collected from Twitter (X) and Reddit. The proposed model achieves an F1-score of 93.51% on the smaller corpus and 90.34% on the larger corpus, obtaining higher scores than the evaluated baselines and reimplemented comparison models under the same experimental setting. These results suggest that the proposed architecture is effective for suicidal ideation detection in Spanish-language social media texts within the two evaluated corpora. The consistent performance obtained on both datasets suggests that the model is capable of capturing relevant linguistic patterns associated with suicidal ideation and highlights its potential to support research and early screening initiatives in Spanish-speaking online environments.

Automatic term extraction is a branch of Natural Language Processing (NLP) used to automatically generate lexicographic materials, such as glossaries, vocabularies, and dictionaries. It allows the creation of standard bases for building unified theories and translations between languages. Scientific literature shows great interest in the construction of automatic term extractors and includes several approaches, tools, algorithms, and methods that can be used for their construction; however, the number of articles in specialized databases is vast, and literature reviews are not recent. This paper presents a systematic literature mapping of the existing material for developing automatic term extractors to provide an overview of approaches, tools, algorithms, and methods used to create them. For this purpose, scientific articles in the domain published between 2015 and 2022 are reviewed and categorized. The mapping results show that among the most used approaches are statistical, with 21.85%; linguistic, with 9.75%; and hybrid, with 68.29%. In addition, there are various computational tools for terminology extraction where authors use different methods for their construction and whose results are measured under the criteria of precision and recall. Finally, 113 documents were selected to answer the research questions and to demonstrate how automatic term extractors are constructed. This paper presents a global summary of primary studies as an essential tool to approach this type of computational system construction.

Automatic Term Extraction is a key area in Natural Language Processing (NLP) focused on generating lexicographic materials essential for standardized theories and cross-lingual translations. While prior studies have surveyed the field, a systematic mapping that concurrently analyzes the relationships between approaches, tools, algorithms and methods has been lacking. This paper fills that gap by presenting a Systematic Literature Mapping (SLM) of 113 scientific papers published between 2015 and 2022. Our analysis provides a novel four-dimensional taxonomy (Approach, Domain, Evaluation, Tool Integration) for Automatic Term Extractors (ATE) and reveals a clear preference for hybrid approaches (50%), which integrate linguistic and statistical approaches, over purely statistical (38.2%) or linguistic (11.8%) ones. The findings show that Python's NLTK library is the most widely adopted tool, while algorithms like C-Value (50%) and TF-IDF (31%) dominate due to their robust performance. The unique contribution of this work is its granular, data-driven overview of the contemporary Automatic Term Extraction landscape, highlighting a critical need for more formal development methodologies and broader domain application. This study serves as a crucial resource for researchers by mapping dominant trends and identifying key research gaps.