For the third project we are taking a data set about comments of a resturant and analyze comments about sentiment and we are thinking of using BERT model.
BERT (Bidirectional Encoder Representations from Transformers), potential natural language processing (NLP) approach. It uses the Transformer design, to depict a breakthrough in language interpretation.
BERT is its bidirectional context awareness, which enables it to take into account both left and right context words at the same time. BERT pre-trains on vast volumes of textual data by anticipating missing words in sentences, in contrast to earlier NLP models that processed text in a unidirectional manner. This allows it to acquire deeply contextualized representations of words. Through this pre-training procedure, BERT gains a thorough grasp subtleties and semantics.
In a variety of NLP tasks, such as named entity recognition, sentiment analysis, and question answering, BERT has shown outstanding performance. It is a foundational model in the area because to its capacity to capture context-rich embeddings, and its pre-trained representations may be adjusted with comparatively little task-specific input for particular downstream tasks. BERT has had a significant influence on the field of NLP, inspiring the creation of many cutting-edge models that draw from its design and guiding ideas.