Sentiment Analysis of E-Commerce Reviews Based on Long Short-Term Memory Networks with Dropout Layer and Optimization

Traditional neural networks cannot accurately analyze the sentiment tendency of the text according to the context of sentences. To address this issue, this paper proposes a recurrent neural network (RNN) model combined with long short-term memory (LTSM) networks. Our method solves the problem that traditional text analysis methods cannot efficiently analyze the emotional tendency of numerous online shopping comments. Firstly, a tokenizer is used to divide sentences into words for emotional analysis, a stop vocabulary table is used to filter out meaningless high-frequency vocabulary, and the Word2Vec is used to vectorize words. Then, the model combining LTSM and RNN is used to capture the emotional features contained in online texts. Finally, the model is optimized to the best performance by choosing different iterations. The experimental results show that the accuracy and F1 value of the proposed model have reached a high rate. The proposed method combining LTSM and RNN can effectively extract the emotional features of Internet text.