Social network textual data classification through a hybrid word embedding approach and Bayesian conditional-based multiple classifiers

Abstract Sentiment analysis (SA) of text holds a pivotal role in today's digital age, particularly within the realm of social media networks. The analysis of textual sentiments emerges as a critical facet of NLP. In social media, individuals extensively engage with a multitude of texts and opinions. SA empowers us to delve into and discover these opinions, sentiments, and viewpoints, thereby extracting valuable insights on a wide array of subjects. The significance of word embeddings for processing textual data lies in their ability to represent words as dense vectors, enabling machines to capture semantic relationships and contextual nuances, thereby enhancing various natural language processing tasks. There are two popular and famous models, BERT and GloVe, for embedding words. Currently, GloVe is considered one of the most precise approaches. However, this method does not take into account the sentiment information present in texts. Consequently, we opted to utilize pre-trained BERT models, which have been trained on extensive text corpora, in combination with the GloVe model to address this limitation. This study leverages a hybrid word embedding model combining BERT and GloVe. Several classifiers are employed to analyze text sentiment. At the decision level, we employ Bayesian Conditional to integrate current results with prior decisions. When combining previous decisions with new ones, the model achieves higher accuracy by refining or adjusting decisions in light of new evidence. Our approach demonstrates notable results, showcasing its practical significance. The results of the experiments on IMDB, Sentiment140, and Twitter US Airline datasets demonstrate that the proposed approach has achieved favorable results, with accuracies of 0.958, 0.925, and 0.946 respectively. These results are considered acceptable when compared to those of other similar studies.