Preservation of emotional context in tweet embeddings on social networking sites

Abstract In communication, emotional information is crucial, yet its preservation in tweet embeddings remains a challenge. This study aims to address this gap by exploring three distinct methods for generating embedding vectors of tweets: word2vec models, pre-trained BERT models, and fine-tuned BERT models. We conducted an analysis to assess the degree to which emotional information is conserved in the resulting embedding vectors. Our findings indicate that the fine-tuned BERT model exhibits a higher level of preservation of emotional information compared to other methods. These results underscore the importance of utilizing advanced natural language processing techniques for preserving emotional context in text data, with potential implications for enhancing sentiment analysis and understanding human communication in social media contexts.