Sentimental Knowledge Graph Analysis of the COVID-19 Pandemic Based on the Official Account of Chinese Universities

Abstract

With the advent of the new media mobile Internet era, the network public opinion in colleges and universities, as an extension of social network public opinion, is also facing a crisis in the prevention, control, and governance system. In this paper, the Fiddler was used to collect the comments and other relevant data of the COVID-19 topic articles on the WeChat Official Accounts of China’s top ten universities in 2020. The BILSTM_LSTM sentiment analysis model was used to analyze the sentiment tendency of the comments, and the LDA topic model was used to mine the topics of the comments with different emotional attributes at different stages of COVID-19. Based on sentiment analysis and text mining, entities and relationships in the theme graph of public opinion events in colleges and universities were identified, and the Neo4j graph database was established to construct the sentimental knowledge graph of the pandemic theme of university public accounts. People’s attitudes in university public opinion are easily influenced by a variety of factors, and the degree of emotional disposition changes over time, with the stage the pandemic is in, and with different commentators; official account opinion topics change with the development of the time stage of the pandemic, and students’ positive and negative comment topics show a diverse trend. By incorporating topic mining into the sentimental knowledge graph, the graph can realize functions such as the emotion retrieval of comments on university public numbers, a source search of security threats in university social networks, and monitoring of comments on public opinion under the theme of the pandemic, which provides new ideas for further exploring the research and governance system of university network public opinion and is conducive to preventing and resolving campus public opinion crises.

Intelligent lead-based bidirectional long short term memory for COVID-19 sentiment analysis

“…The conventional methods utilized for the comparative assessment of the intelligent lead-based BiLSTM are KNN (Shamrat et al 2021 ), stochastic gradient descent-based neural network (SGD-NN) (Akçay 2020 ), Random Forest (Neogi et al 2021 ), TD-LSTM (Wang et al 2016 ), ATAE-LSTM (Tang et al 2015 ), BiLSTM (Li et al 2021 ), Spy based BiLSTM (Pambudi and Kawamura 2022 ), and King based BiLSTM (Soradi-Zeid et al 2020 ).…”

“…The sentiment classification by considering COVID-19 using the intelligent Lead based BiLSTM (Li et al 2021 ) here the classifier is trained using the intelligent lead algorithm to reduce the loss that occurs while training the classifier for making generalizations while testing the unknown data to enhance the accuracy of the analysis. Computation efficiency and reliability can be impacted by a model’s design and choice of configuration parameters.…”

Construction and evaluation of a domain-specific knowledge graph for knowledge discovery

“…Among these, the KG’s most well-known application is semantic search; besides, recommendation, information retrieval and Q&A are also popular tasks using the knowledge application (Chen et al , 2019). Besides, to disclose the vigorous growth of emotion in a specific knowledge domain and organize emotional information, sentimental KG can be used, which builds up from semantic annotation of emotions presented in online reviews along with using Cypher query language to realize variety queries of emotional knowledge (Li et al , 2021).…”

“…Among these, the KG's most well-known application is semantic search; besides, recommendation, information retrieval and Q&A are also popular tasks using the knowledge application (Chen et al, 2019). Besides, to disclose the vigorous growth of emotion in a specific knowledge domain and organize emotional information, sentimental KG can be used, which builds up from semantic annotation of emotions presented in online reviews along with using Cypher query language to realize variety queries of emotional knowledge (Li et al, 2021). Yang et al (2021) present the novel framework to extract the COVID-19 public health evidence KG to support decisionmaking service from evidence querying aspect, returning the query terms' relevant evidence and evidence mapping aspects focusing on interventions in the COVID-19 pandemic effectiveness evaluation results.…”

A Novel Cascade Model for End-to-End Aspect-Based Social Comment Sentiment Analysis

“…The ABSA task is also treated as two-steps containing opinion-target extraction (OTE) and target-oriented sentiment analysis (TSA) by most existing studies. Some studies develop separate methods for OTE [7][8][9][10], while other methods develop for TSA [11][12][13][14][15]35,36]. Li et al, 2018 [7] exploited two useful clues, namely opinion summary and aspect detection history, and presented a new framework for tackling aspect term extraction.…”