A comparison of multiple word embeddings and performance analysis

Abstract

Based on IMDB data sets for sentiment analysis, this research evaluates the performance of two types of neural networks, namely FNN and BERT. According to the experimental findings, both varieties of neural networks performed well (over 80%) on IMDB data sets. BERT performs the best of them all. The version of FNN improves when the number of layers is increased. The causes behind BERT's outstanding performance are then further examined in this research. In this article, it is hypothesized that a few factors, such as feature extraction capability and classification capability, account for BERT's exceptional performance. The results show that for the two coupling variables of BERT assumed in this paper, including feature extraction and classification ability, BERT has obvious advantages in feature extracting, and its classification ability has certain advantages over SVM and MLP. The deep learning model has achieved excellent performance in the sentiment classification task. The next step of this paper will focus on the robustness of the BERT model in sentiment analysis.