Sentiment analysis is animportant part of natural language processing. Initially, the task was addressed using rulebased and statistical tools such as Naïve Bayes and Support Vector Machines (SVM). With the ever-increasing amount of available data, these methods became too simplistic. Deep learning has revolutionized not only the task of sentiment analysis and other tasks in natural language processing. Currently, the best models are context-aware models like BERT. Despite their advent and the advances they have made. The main issues holding the field back is that it's and recognize unstructured data effectively, especially when that unstructured data is concatenated with sarcasm and ambiguity. This study investigated the performance of basic traditional models (Naïve Bayes, Lexicon-based methods, Linear SVM) and modern deep leaning model (BERT) when applied to emotional analysis on unresolved big data issues (customer review data).
Ms. Umme Salmah N (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: