Identification of emotions on Twitter during the 2022 electoral process in Colombia
This work addresses the lack of resources for emotion detection in Colombian Spanish, offering a dataset and methods for analyzing political discourse on social media, though it is incremental as it applies existing models to a new cultural context.
The study tackled emotion detection in Spanish tweets during Colombia's 2022 presidential elections by creating a manually labeled corpus and comparing BERT models with GPT-3.5 in few-shot learning, achieving results that demonstrate the feasibility of such analysis but without specific performance numbers provided.
The study of Twitter as a means for analyzing social phenomena has gained interest in recent years due to the availability of large amounts of data in a relatively spontaneous environment. Within opinion-mining tasks, emotion detection is specially relevant, as it allows for the identification of people's subjective responses to different social events in a more granular way than traditional sentiment analysis based on polarity. In the particular case of political events, the analysis of emotions in social networks can provide valuable information on the perception of candidates, proposals, and other important aspects of the public debate. In spite of this importance, there are few studies on emotion detection in Spanish and, to the best of our knowledge, few resources are public for opinion mining in Colombian Spanish, highlighting the need for generating resources addressing the specific cultural characteristics of this variety. In this work, we present a small corpus of tweets in Spanish related to the 2022 Colombian presidential elections, manually labeled with emotions using a fine-grained taxonomy. We perform classification experiments using supervised state-of-the-art models (BERT models) and compare them with GPT-3.5 in few-shot learning settings. We make our dataset and code publicly available for research purposes.