[1]吴加辉,加云岗,王志晓,等.基于深度学习的微博疫情舆情文本情感分析[J].计算机技术与发展,2024,34(07):175-183.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0087]
　WU Jia-hui,JIA Yun-gang,WANG Zhi-xiao,et al.Sentiment Analysis of Weibo Epidemic Public Opinion Text Based on Deep Learning[J].,2024,34(07):175-183.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0087]
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基于深度学习的微博疫情舆情文本情感分析

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年07期
页码:
175-183
栏目:
人工智能
出版日期:
2024-07-10

文章信息/Info

Title:
Sentiment Analysis of Weibo Epidemic Public Opinion Text Based on Deep Learning
文章编号:
1673-629X(2024)07-0175-09
作者:
吴加辉1; 加云岗1; 王志晓2; 张九龙2; 闫文耀3; 高昂4; 车少鹏5
1. 西安工程大学 计算机科学学院,陕西 西安 710600; 2. 西安理工大学 计算机科学与工程学院,陕西 西安 710048; 3. 延安大学 西安创新学院,陕西 西安 710100; 4. 国家卫星气象中心,北京 100080; 5. 清华大学 新闻与传播学院,北京 100084
Author(s):
WU Jia-hui1; JIA Yun-gang1; WANG Zhi-xiao2; ZHANG Jiu-long2; YAN Wen-yao3; GAO Ang4; CHE Shao-peng5
1. School of Computer Science,Xi'an Polytechnic University,Xi'an 710600,China; 2. School of Computer Science and Engineering,Xi'an University of Technology,Xi'an 710048,China; 3. School of Xi'an Innovation,Yan'an University,Xi'an 710100,China; 4. National Satellite Meteorological Centre,Beijing 100080,China; 5. School of Journalism and Communication,Tsinghua University,Beijing 100084,China
关键词:
RoBERTa; 情感分析; 特征提取; 词向量; 注意力机制; BiGRU
Keywords:
RoBERTa; sentiment analysis; feature extraction; word vectors; attention mechanism; BiGRU
分类号:
TP391
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0087
摘要:
舆论情感分析重点研究公众对于公共事件的情感偏向,其中涉及公共卫生事件的舆论会直接影响社会稳定,所以对于微博的情感分析尤为重要。 该文采取有关疫情方面的文本数据集,使用 RoBERTa 和 BiGRU 以及双层 Attention 结合的 RoBERTa-BDA(RoBERTa-BiGRU-Double Attention)模型作为整体结构。 首先使用 RoBERTa 获取了蕴含文本上下文信息的词嵌入表示,其次使用 BiGRU 得到字符表示,然后使用注意力机制计算各个字符对于全局的影响,再使用 BiGRU得到句子表示,最后使用 Attention 机制计算出每个字符对于其所在的句子的权重占比,得出全文的文本表示,并通过softmax 函数对其进行情感分析。 为了验证 RoBERTa-BDA 模型的有效性,设计三种实验,在不同词向量对比实验中,RoBERTa 对比 BERT 中 Macro F1 和 Micro F1 值提高了 0. 42 百分点和 0. 84 百分点,在不同特征提取层模型对比实验中,BiGRU-Double Attention 对比 BiGRU-Attention 提高了 3. 62 百分点和 1. 34 百分点,在跨平台对比实验中,RoBERTa-BDA 在贴吧平台的 Macro F1 和 Micro F1 对比微博平台仅仅降低 1. 29 百分点 和 2. 88 百分点。
Abstract:
Public opinion sentiment analysis focuses on studying the public's emotional bias towards public events. Public opinions involving public health events will directly affect social stability,so sentiment analysis on Weibo is particularly important. We take text data sets related to the epidemic,and use RoBERTa,BiGRU,and the RoBERTa-BDA (RoBERTa-BiGRU-Double Attention) model combined with double-layer Attention as the overall structure. Firstly,RoBERTa is used to obtain word embedding representation of textual context information. Secondly,BiGRU is used to obtain the character representation,then the attention mechanism is used to calculate the global impact of each character, and then BiGRU is used to obtain the sentence representation. Finally, the Attention mechanism is used to calculate the weight ratio of each character to the sentence in which it is located,and the text representation of the full text is obtained, and the sentiment analysis is carried out through softmax function. In order to verify the effectiveness of the RoBERTa-BDA model,three experiments were designed. In the comparison experiment of different word vectors,the Macro F1 and Micro F1 values in RoBERTa compared with BERT increased by 0. 42 percentage points and 0. 84 percentage points,respectively,in different feature extraction layers. In the model comparison experiment,BiGRU-Double Attention increased by 3. 62 percentage points and 1. 34 percentage points compared to BiGRU - Attention. In the cross - platform comparison experiment, RoBERTa - BDA only decreased by 1. 29 percentage points and 2. 88 percentage points on the Tieba platform Macro F1 and Micro F1 compared to the Weibo platform.

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更新日期/Last Update: 2024-07-10