Processamento de linguagem natural em análise de mídias sociais: um mapeamento sistemático

Abstract

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.