融合情感词典的改进BiLSTM-CNN+Attention情感分类算法

传统机器学习和深度学习模型在处理情感分类任务时会忽略情感特征词的强度，情感语义关系单薄，造成情感分类的精准度不高。本文提出一种融合情感词典的改进型BiLSTM-CNN+Attention情感分类算法。首先，该算法通过融合情感词典优化特征词的权重；其次，利用卷积神经网络(CNN)提取局部特征，利用双向长短时记忆网络(BiLSTM)高效提取上下文语义特征和长距离依赖关系；再结合注意力机制对情感特征加成；最后由Softmax分类器实现文本情感预测。实验结果表明，本文提出的情感分类算法在精确率、召回率和F值上均有较大提升。相较于TextCNN、BiLSTM、LSTM、CNN和随机森林模型，本文方法的F值分别提高2.35%、3.63%、4.36%、2.72%和6.35%。这表明该方法能够充分融合情感特征词的权重，利用上下文语义特征，提高情感分类性能。该方法具有一定的学术价值和应用前景。

Improved BiLSTM-CNN+Attention Sentiment Classification Algorithm Fused with Sentiment Dictionary

Traditional machine learning and deep learning models ignore the strength of emotional feature words when processing emotion classification tasks. As a result, the emotional semantic relationship is poor, resulting in low accuracy of emotion classification. To this end, this paper proposes an improved BiLSTM-CNN+Attention sentiment classification algorithm with a sentiment dictionary. Firstly, the algorithm optimizes the weights of feature words by fusion of sentiment dictionaries. Secondly, it uses a convolutional neural network (CNN) to extract local features and uses a bidirectional long and short-term memory network (BiLSTM) to extract contextual semantic features and long-distance dependencies efficiently. Then, this model combines the attention mechanism to add to the emotion features. Finally, the Softmax classifier realizes the text emotion prediction. Experimental results show that the sentiment classification algorithm proposed in this paper dramatically improves precision, recall, and F-measure. Compared with TextCNN, BiLSTM, LSTM, CNN, and random forest models, the F-measure of this method is increased by 2.35%, 3.63%, 4.36%, 2.72%, and 6.35%, respectively. In short, the proposed method can fully integrate the weights of emotional feature words, use contextual semantic features, and improve the performance of emotional classification. Therefore, this method has specific educational value and application prospects.