Multi-label Emotion Classification of COVID-19 Tweets with Deep Learning and Topic Modelling

The COVID-19 pandemic has become one of the severe diseases in recent years. As it majorly affects the common livelihood of people across the universe, it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak. The public opinions are been shared enormously in microblogging media like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics, sports, entertainment etc., This work presents a combination of Intensity Based Emotion Classification Convolution Neural Network (IBEC-CNN) model and Non-negative Matrix Factorization (NMF) for detecting and analyzing the different topics discussed in the COVID-19 tweets as well the intensity of the emotional content of those tweets. The topics were identified using NMF and the emotions are classified using pretrained IBEC-CNN, based on predefined intensity scores. The research aimed at identifying the emotions in the Indian tweets related to COVID-19 and producing a list of topics discussed by the users during the COVID-19 pandemic. Using the Twitter Application Programming Interface (Twitter API), huge numbers of COVID-19 tweets are retrieved during January and July 2020. The extracted tweets are analyzed for emotions fear, joy, sadness and trust with proposed Intensity Based Emotion Classification Convolution Neural Network (IBEC-CNN) model which is pretrained. The classified tweets are given an intensity score varies from 1 to 3, with 1 being low intensity for the emotion, 2 being the moderate and 3 being the high intensity. To identify the topics in the tweets and the themes of those topics, Non-negative Matrix Factorization (NMF) has been employed. Analysis of emotions of COVID-19 tweets has identified, that the count of positive tweets is more than that of count of negative tweets during the period considered and the negative tweets related to COVID-19 is less than 5%. Also, more than 75% negative tweets expressed sadness, fear are of low intensity. A qualitative analysis has also been conducted and the topics detected are grouped into themes such as economic impacts, case reports, treatments, entertainment and vaccination. The results of analysis show that the issues related to the pandemic are expressed different emotions in twitter which helps in interpreting the public insights during the pandemic and these results are beneficial for planning the dissemination of factual health statistics to build the trust of the people. The performance comparison shows that the proposed IBEC-CNN model outperforms the conventional models and achieved 83.71% accuracy. The % of COVID-19 tweets that discussed the different topics vary from 7.45% to 26.43% on topics economy, Statistics on cases, Government/Politics, Entertainment, Lockdown, Treatments and Virtual Events. The least number of tweets discussed on politics/government on the other hand the tweets discussed most about treatments. © 2023 CRL Publishing. All rights reserved.

What Do Users Think of the Digital Library? Analyzing Community's Sentiment during Covid-19

During the pandemic, a consortium of libraries in the Milan area (Italy) interviewed the community and applied sentiment analysis to quickly understand the community's feelings toward online services. The model compares both sentiment analysis and topic detection using algorithms that identify sentiments and topics. The results highlight the preference for paper books but the interest in learning more about digital services. [ FROM AUTHOR]

Sentiment Analysis of Users' Reactions on Social Media during the Pandemic

During the outbreak of the COVID-19 pandemic, social networks became the preeminent medium for communication, social discussion, and entertainment. Social network users are regularly expressing their opinions about the impacts of the coronavirus pandemic. Therefore, social networks serve as a reliable source for studying the topics, emotions, and attitudes of users that have been discussed during the pandemic. In this paper, we investigate the reactions and attitudes of people towards topics raised on social media platforms. We collected data of two large-scale COVID-19 datasets from Twitter and Instagram for six and three months, respectively. This paper analyzes the reaction of social network users in terms of different aspects including sentiment analysis, topic detection, emotions, and the geo-temporal characteristics of our dataset. We show that the dominant sentiment reactions on social media are neutral, while the most discussed topics by social network users are about health issues. This paper examines the countries that attracted a higher number of posts and reactions from people, as well as the distribution of health-related topics discussed in the most mentioned countries. We shed light on the temporal shift of topics over countries. Our results show that posts from the top-mentioned countries influence and attract more reactions worldwide than posts from other parts of the world.

Analysis of Network Public Opinion in New Media Based on BP Neural Network Algorithm

With the rapid development of Internet technology, new media is more and more favored by people and has become an important medium to control online public opinion. Social public opinion caused by new media is also more and more concerned by all walks of life. New media has the characteristics of fast information dissemination, wide dissemination range, and strong arbitrariness of news release. Positive online public voice or negative online public voice will have a very different impact on people's lives. Some negative online public voice may even constitute a social crisis and seriously affect social public security. In order to analyze and predict the development trend of new media network public opinion, this paper presents a design of improved BP neural network model based on genetic algorithm, which is used to analyze public opinion in new media network. Experimental results show that this way has stronger processing ability and higher warning accuracy for online public opinion event index data. It can provide certain theoretical basis and data support for relevant departments to effectively prevent and manage new media network public opinion events.

An Analysis on the Weibo Topic Detection Based on K-means Algorithm

Sina Weibo, as a platform for netizens to express their opinions, generates a large amount of public opinion data and constantly generates new topics. How to detect new and hot topics on Weibo is a meaningful studied issue. Document Clustering is a widely studied problem in Text Categorization. K-means is one of the most famous unsupervised learning algorithms, partitions a given dataset into disjoint clusters following a simple and easy way. But the traditional K-means algorithm assigns initial centroids randomly, which cannot guarantee to choose the maximum dissimilar documents as the centroids for the clusters. A modified K-means algorithm is proposed, which uses Jaccard distance measure for assigning the most dissimilar k documents as centroids, and uses Word2vec as the Chinese text vectorization model. The experimental results demonstrate that the proposed K-means algorithm improves the clustering performance, and is able to detect new and hot topics based on Weibo COVID-19 data. © 2022 IEEE.

Thematic Analysis as a New Culturomic Tool: The Social Media Coverage on COVID-19 Pandemic in Italy

The COVID-19 pandemic influenced people’s everyday lives because of the health emergency and the resulting socio-economic crisis. People use social media to share experiences and search for information about the disease more than before. This paper aims at analysing the discourse on COVID-19 developed in 2020 by Italian tweeters, creating a digital storytelling of the pandemic. Employing thematic analysis, an approach used in bibliometrics to highlight the conceptual structure of a research domain, different time slices have been described, bringing out the most discussed topics. The graphical mapping of these topics allowed obtaining an easily readable representation of the discourse, paving the way for novel uses of thematic analyses in social sciences.

Retrospective analysis of controversial topics on COVID-19 in Japan

For efficient policy-making, a thorough recognition of controversial topics is crucial because the cost of unmitigated controversies would be extremely high for society. However, identifying controversial topics is costly. In this paper, we proposed a framework to search for controversial topics comprehensively. We then conducted a retrospective analysis of the controversial topics of COVID-19 with data obtained via Twitter in Japan as a case study of the framework. The results show that the proposed framework can effectively detect controversial topics that reflect current reality. Controversial topics tend to be about the government, medical matters, economy, and education;moreover, the controversy score had a low correlation with the traditional indicators-scale and sentiment of the topics-which suggests that the controversy score is a potentially important indicator to be obtained. We also discussed the difference between highly controversial topics and less controversial ones despite their large scale and sentiment. © 2021 ACM.

Evolution of topics and hate speech in retweet network communities.

Twitter data exhibits several dimensions worth exploring: a network dimension in the form of links between the users, textual content of the tweets posted, and a temporal dimension as the time-stamped sequence of tweets and their retweets. In the paper, we combine analyses along all three dimensions: temporal evolution of retweet networks and communities, contents in terms of hate speech, and discussion topics. We apply the methods to a comprehensive set of all Slovenian tweets collected in the years 2018-2020. We find that politics and ideology are the prevailing topics despite the emergence of the Covid-19 pandemic. These two topics also attract the highest proportion of unacceptable tweets. Through time, the membership of retweet communities changes, but their topic distribution remains remarkably stable. Some retweet communities are strongly linked by external retweet influence and form super-communities. The super-community membership closely corresponds to the topic distribution: communities from the same super-community are very similar by the topic distribution, and communities from different super-communities are quite different in terms of discussion topics. However, we also find that even communities from the same super-community differ considerably in the proportion of unacceptable tweets they post.

An Infoveillance System for Detecting and Tracking Relevant Topics From Italian Tweets During the COVID-19 Event.

The year 2020 opened with a dramatic epidemic caused by a new species of coronavirus that soon has been declared a pandemic by the WHO due to the high number of deaths and the critical mass of worldwide hospitalized patients, of order of millions. The COVID-19 pandemic has forced the governments of hundreds of countries to apply several heavy restrictions in the citizens' socio-economic life. Italy was one of the most affected countries with long-term restrictions, impacting the socio-economic tissue. During this lockdown period, people got informed mostly on Online Social Media, where a heated debate followed all main ongoing events. In this scenario, the following study presents an in-depth analysis of the main emergent topics discussed during the lockdown phase within the Italian Twitter community. The analysis has been conducted through a general purpose methodological framework, grounded on a biological metaphor and on a chain of NLP and graph analysis techniques, in charge of detecting and tracking emerging topics in Online Social Media, e.g. streams of Twitter data. A term-frequency analysis in subsequent time slots is pipelined with nutrition and energy metrics for computing hot terms by also exploiting the tweets quality information, such as the social influence of the users. Finally, a co-occurrence analysis is adopted for building a topic graph where emerging topics are suitably selected. We demonstrate via a careful parameter setting the effectiveness of the topic tracking system, tailored to the current Twitter standard API restrictions, in capturing the main sociopolitical events that occurred during this dramatic phase.

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis.

BACKGROUND: As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter. OBJECTIVE: The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media. METHODS: To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity. RESULTS: After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy. CONCLUSIONS: This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust.

Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA.

Twitter is a social media platform with more than 500 million users worldwide. It has become a tool for spreading the news, discussing ideas and comments on world events. Twitter is also an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources. It is a challenge identifying interesting and useful content from large text-streams in different languages, few works have explored languages other than English. In this paper, we use topic identification and sentiment analysis to explore a large number of tweets in both countries with a high number of spreading and deaths by COVID-19, Brazil, and the USA. We employ 3,332,565 tweets in English and 3,155,277 tweets in Portuguese to compare and discuss the effectiveness of topic identification and sentiment analysis in both languages. We ranked ten topics and analyzed the content discussed on Twitter for four months providing an assessment of the discourse evolution over time. The topics we identified were representative of the news outlets during April and August in both countries. We contribute to the study of the Portuguese language, to the analysis of sentiment trends over a long period and their relation to announced news, and the comparison of the human behavior in two different geographical locations affected by this pandemic. It is important to understand public reactions, information dissemination and consensus building in all major forms, including social media in different countries.