摘要
情感分类技术在舆论评价和商品评价等诸多领域均有广泛使用,对自然语言处理领域来说具有很重要的研究意义。在当前的社交网络文本中,用户不仅仅使用文字来传达情感,其文本中的表情元素也带有浓厚的情感色彩。传统情感分析模型容易忽略表情元素,从而导致模型未能正确判断文本情感。文中将BERT预训练模型和长短时记忆网络相结合,运用带表情元素的weibo_senti_100k数据集实现一个针对微博评论的情感二分类模型。BERT-LSTM模型利用BERT嵌入层对预处理后的句子进行分割并将其转换为动态词向量,结合LSTM模型提取文本和表情元素的特征,最后以预测评论文本的情感极性。实验验证表情元素的重要性和BERT-LSTM模型情感分类的有效性,结果表明同时考虑文字和表情元素相较于纯文字来说模型分类准确率提高20%,BERT-LSTM模型的分类准确率为98.31%、F1值为98.28%,相比传统机器学习模型和其他深度学习模型在最终结果上表现出明显优势。
Sentiment classification technology is widely used in many fields such as public opinion evaluation and commodity evaluation,which is of great research significance in the field of Natural Language Processing.In the current social network text,users not only use text to convey emotions,but also have strong emotional color in their text with emoji elements.Traditional sentiment analysis models are prone to ignoring emoji elements,resulting in the model not being able to accurately judge text emotions.This article combines the BERT pre-training model with LSTM model,using weibo_senti_100k dataset with emoji elements to implement a sentiment binary classification model for Weibo comments.The BERT-LSTM model utilizes the BERT embedding layer to segment the preprocessed sentences and convert them into dynamic word vectors.It combines the LSTM model to extract features of text and emoji elements,and finally predicts the emotional polarity of the comment text.The experimental verification of the importance of emoji elements and the effectiveness of the BERT-LSTM model for emotion classification.It showed that considering both text and emoji elements improved the classification accuracy of the model by 20%compared to pure text.The BERT-LSTM model has the accuracy of 98.31%and the F1 value of 98.28%,showing significant advantages over traditional machine learning models and other deep learning models in the final results.
作者
蒲秋梅
黄方俐
王辉
PU Qiu-mei;HUANG Fang-li;WANG Hui(Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE,Minzu University of China,Beijing 100081,China;School of Information Engineering,Minzu University of China,Beijing 100081,China)
出处
《中国电子科学研究院学报》
北大核心
2023年第10期912-920,共9页
Journal of China Academy of Electronics and Information Technology
基金
国家社会科学基金资助项目(20BGL251)。
