关键词: 情感分析, 深度学习, BILSTM-CRF模型, BERT, 注意力机制

Abstract:

Syntactic features and BERT word embedding model is integrated into the deep learning network to achieve the fine-grained commodity evaluation analysis by taking advantage of the BERT pre-training model. A two-stage fine-grained commodity evaluation sentiment analysis model based on deep learning is proposed. ?Firstly, the BILSTM-CRF attention mechanism model which combines syntactic features and BERT word embedding is used to extract commodity entities, attributes, and emotional words in user reviews. Then, the BILSTM model is applied to analyze the sentiment of the extracted results. The F1 value of feature extraction on SemEval-2016 Task 5 and COAE Task3 commodity evaluation dataset reaches 88.2%, which is 4.8 percentage points and 2.3 percentage points higher than that of the BILSTM model and BILSTM-CRF model, respectively. The accuracy of sentiment classification is up to 88.5%, which is 8 percentage points higher than ordinary RNN, and 15 percentage points higher than traditional machine learning methods, such as support vector machine and naive Bayes. To corroborate the deep learning model which integrates syntactic features and BERT word embedding is superior in the sentiment analysis of fine-grained commodity evaluation by analyzing the complexity of the model.

Key words: sentiment analysis, deep learning, BILSTM-CRF model, BERT, attention mechanism