Abstract
Text Sentiment Classification (TSC) is an important task in the field of Natural Language Processing (NLP). Previous TSC methods cannot dynamically obtain the semantics of words based on the context, which leads to deviations in the results of TSC. However, the proposal and application of Bidirectional Encoder Representation from Transformers (BERT) has greatly improved the accuracy of TSC tasks. BERT can dynamically perceive contextual information to obtain more accurate classification results. In this paper, we build different BERT-based models and conduct our experiments on the benchmark dataset Internet Movie Database (IMDB). The experimental result shows that even with a simple linear layer, our BERT-based model still improves the F1-score by 2.01% compared with the best performing model in the baselines.
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