Approval network: a novel approach for sentiment analysis in social networks

Fersini, Elisabetta; Pozzi, Federico; Messina, Enza

doi:10.1007/s11280-016-0419-8

Cited by 20 publications

(5 citation statements)

References 49 publications

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“…As many countries start to experience a second wave of COVID-19 outbreaks, social media platforms can collect large amounts of information that reflect public behavior and attitudes. By analyzing this data, it is possible to identify the demands and behavioral characteristics of citizens [ 40 ].…”

Section: Discussionmentioning

confidence: 99%

Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data

Wang¹,

Ying²,

Wei³

et al. 2020

J Med Internet Res

104

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Background The COVID-19 pandemic has created a global health crisis that is affecting economies and societies worldwide. During times of uncertainty and unexpected change, people have turned to social media platforms as communication tools and primary information sources. Platforms such as Twitter and Sina Weibo have allowed communities to share discussion and emotional support; they also play important roles for individuals, governments, and organizations in exchanging information and expressing opinions. However, research that studies the main concerns expressed by social media users during the pandemic is limited. Objective The aim of this study was to examine the main concerns raised and discussed by citizens on Sina Weibo, the largest social media platform in China, during the COVID-19 pandemic. Methods We used a web crawler tool and a set of predefined search terms (New Coronavirus Pneumonia, New Coronavirus, and COVID-19) to investigate concerns raised by Sina Weibo users. Textual information and metadata (number of likes, comments, retweets, publishing time, and publishing location) of microblog posts published between December 1, 2019, and July 32, 2020, were collected. After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. We analyzed the emotional tendencies of the topics, calculated the proportional distribution of the topics, performed user behavior analysis on the topics using data collected from the number of likes, comments, and retweets, and studied the changes in user concerns and differences in participation between citizens living in different regions of mainland China. Results Based on the 203,191 eligible microblog posts collected, we identified 17 topics and grouped them into 8 themes. These topics were pandemic statistics, domestic epidemic, epidemics in other countries worldwide, COVID-19 treatments, medical resources, economic shock, quarantine and investigation, patients’ outcry for help, work and production resumption, psychological influence, joint prevention and control, material donation, epidemics in neighboring countries, vaccine development, fueling and saluting antiepidemic action, detection, and study resumption. The mean sentiment was positive for 11 topics and negative for 6 topics. The topic with the highest mean of retweets was domestic epidemic, while the topic with the highest mean of likes was quarantine and investigation. Conclusions Concerns expressed by social media users are highly correlated with the evolution of the global pandemic. During the COVID-19 pandemic, social media has provided a platform for Chinese government departments and organizations to better understand public concerns and demands. Similarly, social media has provided channels to disseminate information about epidemic prevention and has influenced public attitudes and behaviors. Government departments, especially those related to health, can create appropriate policies in a timely manner through monitoring social media platforms to guide public opinion and behavior during epidemics.

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Section: Discussionmentioning

confidence: 99%

Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data

Wang¹,

Ying²,

Wei³

et al. 2020

J Med Internet Res

104

View full text Add to dashboard Cite

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“…In alternative, from a methodological point of view, the disambiguation process could regard more than two possible alternatives, solving the problem of the not imputed values. Thirdly, similar considerations could regard the introduction of this procedure for data represented by an adjacent matrix in which the missing observation is the absence of link between two subjects, using for example a dual representation or different weights for the "Likes" as in Fersini et al (2017). Finally, a different point of view could be accomplished comparing these results with approaches based on recommender systems.…”

Section: Final Remarksmentioning

confidence: 99%

Missing values and data enrichment: an application to social media liking

2022

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In the big data context, it is very frequent to manage the analysis of missing values. This is especially relevant in the field of statistical analysis, where this represents a thorny issue. This study proposes a strategy for data enrichment in presence of sparse matrices. The research objective consists in the evaluation of a possible distinction of behaviour among observations in sparse matrices with missing data. After selecting among the multiple imputation methods, an innovative technique will be presented to impute missing observations as a negative position or a neutral opinion. This method has been applied to a dataset measuring the interaction between users and social network pages for some Italian newspapers.

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“…The third method performs sentiment analysis by comparing it with a trained data set [17]. In addition, methods that perform positive / negative information analysis at the user level have also been proposed [18]. Analysis of the effects of users on social networks on other users is one of the studies used with these methods [19].…”

Section: Related Workmentioning

confidence: 99%

Finding the Relationship Between News and Social Media Users’ Emotions in the COVID-19 Process

Müngen

Aygün

Kaya

2020

Sakarya University Journal of Computer and Information Sciences

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Nowadays, social media and online sharing sites are frequently used to share thoughts about daily events. Thanks to the posts made by internet users on these platforms, first, quite big data is generated to interpret the agenda. More than 10,000 comments of more than 5000 users made about COVID-19 from online websites between 15 March and 15 May were collected in this study. Then, emotional analysis on these comments was carried out with BERT, GRU, LSTM and TF-IDF methods. The changes in the amount of user comments and the emotions reflected by the comments have been associated with the actual events of these dates. It has been determined which types of events affect users more. In addition, the emotional response changes of the users to the official COVID-19 statistics were measured and the peak points of the emotional changes were determined. Finally, the emotion classification methods applied were evaluated by user questionnaires and their successes were determined according to F-Measure.

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Approval network: a novel approach for sentiment analysis in social networks

Cited by 20 publications

References 49 publications

Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data

Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data

Missing values and data enrichment: an application to social media liking

Finding the Relationship Between News and Social Media Users’ Emotions in the COVID-19 Process

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