Network Public Opinion Monitoring System for Agriculture Products Based on Big Data

Liu, He; Yu, Zekun; Zhong, Xiangzhi; Yu, Honggang

doi:10.1155/2021/9976001

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Te increasing popularity of the Internet and the number of netizens make society enter the information explosion era. Because the public opinion environment of the social networking platform is more open, free, and private than that of real society, and because public publishing and obtaining information in the network is more convenient and faster than exploring, publicizing, or querying information in the real world, more and more people pay attention to daily hot spots or public events through the Internet [1][2][3][4][5]. When people fnd the information they are interested in in the process of browsing the Internet, many people will express their own opinions and opinions on the content of the information, and the information containing the emotional tendencies and subjective attitudes of netizens constitutes a new network public opinion information [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Network Public Opinion Risk Prediction and Judgment Based on Deep Learning: A Model of Text Sentiment Analysis

Yang

2022

Computational Intelligence and Neuroscience

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Under the background of the gradual development and popularization of mobile Internet information technology, this paper realizes network public opinion monitoring and emotion analysis based on the deep learning method, aiming at the research needs of people’s ideological changes and emotional trends. Aiming at the shortcomings of sentiment dictionaries or machine learning methods in sentiment analysis tasks, this paper builds a sentiment classification model based on deep learning methods. First, the current main text preprocessing methods are introduced, and then a sentiment classification model, BCBL, is proposed, combining BERT, CNN, and Bi LSTM. Compared with traditional models, BCBL can better complete text sentiment classification tasks on standard datasets. Next, in view of the problem that BCBL does not consider the distribution of vocabulary weights, an attention mechanism is introduced to improve BCBL, and then the BCBL-Att model is proposed. Set up multiple sets of comparative experiments again and find that the classification effect and overall performance of BCBL-Att on standard datasets are better than BCBL, indicating that BCBL-Att has more advantages in text sentiment classification tasks.

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Section: Introductionmentioning

confidence: 99%

Network Public Opinion Risk Prediction and Judgment Based on Deep Learning: A Model of Text Sentiment Analysis

Yang

2022

Computational Intelligence and Neuroscience

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“…Te Internet of Tings was explored for campus public opinion monitoring [19]. An online public opinion monitoring system for agricultural products was established to help the agricultural sector shift from passive public opinion to active public opinion [20]. A GloVe-LSTM model was established to monitor COVID-19 related tweets [21].…”

Section: Introductionmentioning

confidence: 99%

Industrial Chain Disruption Events Monitoring with Deep Learning Methods: A Practical Application in China

Tang,

Yuan,

Gong

2023

International Journal of Intelligent Systems

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Globalization has made the industrial chain longer and more complex, resulting in greater vulnerability to emergencies, such as the COVID-19 pandemic, earthquakes, and wars. Emergencies will lead to plant shutdowns, supply shortages, market blockades, and other risk events, leading to large-scale supply chain disruption, also known as industrial chain disruption. The safety and stability of the industrial chain are the foundation of national economic stability. All countries attach great importance to risk monitoring leading to the interruption of the industrial chain. However, at present, the risk monitoring method of the industrial chain is mainly to screen out the risk events that may cause the industrial chain disruption from news by manually monitoring the news media. It is of great significance to establish an efficient automatic monitoring system for industrial chain disruption events (ICDE). In this paper, an ICDE monitoring model is proposed to identify ICDE automatically using deep learning technology. The ICDE monitoring model consists of an ICDE identification model and an ICDE correlation model. The former identifies risk events from online news through the Ernie model, while the latter matches risk events with industrial chain nodes through similar nodes and virtual nodes. Similar nodes refer to synonyms in industrial chain nodes. Virtual nodes refer to the words that appear in a large number in the news and do not exist in the industrial chain, but they form an inclusive relationship with the nodes of the industrial chain. Finally, the model is applied to the new energy vehicle industry chain as an example. The application results show that the model can monitor ICDE on each node of the industry chain in real time, and the identification accuracy of ICDE is 92%. Through the ICDE monitoring model, the national or local government can formulate measures to reduce industrial losses in time and track the risk status of the industrial chain in real time.

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“…Monitoring NPOs could correctly guide the NPO on agricultural products and prevent the spread of negative NPO in time, reducing the negative impact of NPO hotspot events. Liu et al [ 8 ] developed a big data-based NPO monitoring system for agricultural products. The model could collect, process, and analyze data in time, discover and track hot topics, and calculate and visualize the polarity of NPOs.…”

Section: Introductionmentioning

confidence: 99%

Application of edge computing combined with deep learning model in the dynamic evolution of network public opinion in emergencies

Chen

Zhang

2022

J Supercomput

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Network Public Opinion Monitoring System for Agriculture Products Based on Big Data

Cited by 6 publications

References 13 publications

Network Public Opinion Risk Prediction and Judgment Based on Deep Learning: A Model of Text Sentiment Analysis

Network Public Opinion Risk Prediction and Judgment Based on Deep Learning: A Model of Text Sentiment Analysis

Industrial Chain Disruption Events Monitoring with Deep Learning Methods: A Practical Application in China

Application of edge computing combined with deep learning model in the dynamic evolution of network public opinion in emergencies

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