Processamento de linguagem natural em análise de mídias sociais: um mapeamento sistemático
Fecha
2023Autor
http://lattes.cnpq.br/2201818644935012
https://orcid.org/0000-0003-1143-507X
ARAÚJO, Gabriele de Sousa
Metadatos
Mostrar el registro completo del ítemResumen
The number of social media platforms has increased signifiantly, as has the number of active
users. More than 18.2 million text messages are transmitted every minute on these platforms.
Given the amount of data available, Natural Language Processing (NLP) techniques have been
used by several researchers to analyze this large amount of unstructured data. Thus, it is essential
to understand social media analysis’s main trends and challenges, especially in scientifi events.
In this perspective, this study presents a systematic mapping of PLN for social media analysis in
works published in fie academic events: BRACIS, BraSNAM, ENIAC, STIL, and PROPOR.
These events were chosen due to their relevance. The study aims to identify the main tools and
techniques used, tasks performed, data sources, and evaluation measures. For this purpose, 186
studies were analyzed and carefully selected among the 654 articles published in these events
in the three years (2020 to 2022). The results show a clipping of the current scenario on the
subject and point out areas that can be improved in future research using techniques such as
text classifiation, sentiment analysis, and recognition of named entities. Thus, this work can be
helpful for academics interested in exploring the potential of these tools and techniques, having
a clear view of gaps, challenges, and research opportunities in this area, as well as analyzing the
current scenario in research involving NLP and social media. Nevertheless, guide the productive
sector in this knowledge transfer, reducing the gap between the state of the art and practice and
increasing the competitiveness and innovation of social media analysis tools.