Today social media became a communication line among people to share their happiness,sadness,and anger with their end-users.It is necessary to know people’s emotions are very important to identify depressed people fr...Today social media became a communication line among people to share their happiness,sadness,and anger with their end-users.It is necessary to know people’s emotions are very important to identify depressed people from their messages.Early depression detection helps to save people’s lives and other dangerous mental diseases.There are many intelligent algorithms for predicting depression with high accuracy,but they lack the definition of such cases.Several machine learning methods help to identify depressed people.But the accuracy of existing methods was not satisfactory.To overcome this issue,the deep learning method is used in the proposed method for depression detection.In this paper,a novel Deep Learning Multi-Aspect Depression Detection with Hierarchical Atten-tion Network(MDHAN)is used for classifying the depression data.Initially,the Twitter data was preprocessed by tokenization,punctuation mark removal,stop word removal,stemming,and lemmatization.The Adaptive Particle and grey Wolf optimization methods are used for feature selection.The MDHAN classifies the Twitter data and predicts the depressed and non-depressed users.Finally,the proposed method is compared with existing methods such as Convolutional Neur-al Network(CNN),Support Vector Machine(SVM),Minimum Description Length(MDL),and MDHAN.The suggested MDH-PWO architecture gains 99.86%accuracy,more significant than frequency-based deep learning models,with a lower false-positive rate.The experimental result shows that the proposed method achieves better accuracy,precision,recall,and F1-measure.It also mini-mizes the execution time.展开更多
【目的】针对方面情感分类输入类别在不同领域之间差异较大,汽车用户评论文本语义信息不全,语义特征难以提取等问题,提出基于双通道输入的并行双向编码表征(bidirectional encoder representation from transformers,BERT)双向长短期记...【目的】针对方面情感分类输入类别在不同领域之间差异较大,汽车用户评论文本语义信息不全,语义特征难以提取等问题,提出基于双通道输入的并行双向编码表征(bidirectional encoder representation from transformers,BERT)双向长短期记忆多头自注意力模型的方面情感分类方法。【方法】首先采用了方面情感和方面抽取的双重标签进行标注;其次通过并行的方面抽取和方面情感分类任务通道,分别使用BERT、双向长短期记忆网络(bidirectional long and short-term memory networks,Bi-LSTM)及多头注意力机制(multihead self-attention,MHSA)提取更深层次的语义信息及近距离和远距离特征信息;最后采用条件随机场(conditional random field,CRF)分类器和Softmax分类器进行分类。【结果】在相关的汽车用户评论文本数据集和多语言混合数据集上,本研究提出的模型相较于主流的方面情感分类方法,具有同步抽取方面词和判断情感极性的能力,且有效提高了方面词抽取和方面情感分类的准确率和F_(1)值。【结论】本研究提出的模型更有利于汽车销售者分析用户评论,同时对识别用户评论文本的情感极性的研究也有一定的参考价值。展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R300),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Today social media became a communication line among people to share their happiness,sadness,and anger with their end-users.It is necessary to know people’s emotions are very important to identify depressed people from their messages.Early depression detection helps to save people’s lives and other dangerous mental diseases.There are many intelligent algorithms for predicting depression with high accuracy,but they lack the definition of such cases.Several machine learning methods help to identify depressed people.But the accuracy of existing methods was not satisfactory.To overcome this issue,the deep learning method is used in the proposed method for depression detection.In this paper,a novel Deep Learning Multi-Aspect Depression Detection with Hierarchical Atten-tion Network(MDHAN)is used for classifying the depression data.Initially,the Twitter data was preprocessed by tokenization,punctuation mark removal,stop word removal,stemming,and lemmatization.The Adaptive Particle and grey Wolf optimization methods are used for feature selection.The MDHAN classifies the Twitter data and predicts the depressed and non-depressed users.Finally,the proposed method is compared with existing methods such as Convolutional Neur-al Network(CNN),Support Vector Machine(SVM),Minimum Description Length(MDL),and MDHAN.The suggested MDH-PWO architecture gains 99.86%accuracy,more significant than frequency-based deep learning models,with a lower false-positive rate.The experimental result shows that the proposed method achieves better accuracy,precision,recall,and F1-measure.It also mini-mizes the execution time.
文摘【目的】针对方面情感分类输入类别在不同领域之间差异较大,汽车用户评论文本语义信息不全,语义特征难以提取等问题,提出基于双通道输入的并行双向编码表征(bidirectional encoder representation from transformers,BERT)双向长短期记忆多头自注意力模型的方面情感分类方法。【方法】首先采用了方面情感和方面抽取的双重标签进行标注;其次通过并行的方面抽取和方面情感分类任务通道,分别使用BERT、双向长短期记忆网络(bidirectional long and short-term memory networks,Bi-LSTM)及多头注意力机制(multihead self-attention,MHSA)提取更深层次的语义信息及近距离和远距离特征信息;最后采用条件随机场(conditional random field,CRF)分类器和Softmax分类器进行分类。【结果】在相关的汽车用户评论文本数据集和多语言混合数据集上,本研究提出的模型相较于主流的方面情感分类方法,具有同步抽取方面词和判断情感极性的能力,且有效提高了方面词抽取和方面情感分类的准确率和F_(1)值。【结论】本研究提出的模型更有利于汽车销售者分析用户评论,同时对识别用户评论文本的情感极性的研究也有一定的参考价值。
