COVID-19 pandemic & cyber security issues: Sentiment analysis and topic modeling approach

IntroductionIn recent years, with the rapid advancement of urbanization in China and the successive implementation of major national strategies such as the Belt and Road Initiative, the Sichuan–Xizang Railway, and the South-to-North Water Diversion Project, the potential risks and losses from geological disasters have continued to rise. Secondary disasters—including landslides, mudslides, and barrier lakes triggered by earthquakes—have significantly intensified the overall impact, posing severe challenges to disaster monitoring, early warning, emergency response, recovery, and reconstruction efforts. In this context, how to leverage new information technologies to achieve in-depth mining and application of geological disaster data has become a critical issue in disaster risk reduction and sustainable crisis management.MethodsThis study focuses on topic modeling and sentiment analysis of disaster-related data, using geological disasters in China as a background. First, it reviews the recent advances in topic modeling and sentiment analysis techniques. Then, based on data characteristics and applicability, two major social media platforms—Weibo (Sina Weibo) and Rednote (Xiaohongshu)—are selected as primary data sources. The advantages of the LDA topic model (e.g., its unlabeled and multi-topic capabilities) and the lightweight processing efficiency of the SnowNLP sentiment analysis algorithm are discussed. As a case study, the “1•07″earthquake in Xigaze, Tibet, in 2025 is analyzed. The LDA model is used to conduct multi-topic classification and clustering visualization of Weibo disaster topic data. Combined with the SnowNLP sentiment analysis algorithm, the phased sentiment evolution judgment application is carried out using the 6-month Rednote comment data.ResultsThe LDA model effectively extracts geological disaster-related themes—such as emergency response and post-disaster recovery—and that sentiment analysis technology can reveal phase-based patterns in public emotions.ConclusionThese findings provide scientific support for geological disaster emergency management and public opinion guidance. The research also expands the application potential of topic modeling and sentiment analysis in the field of geological disasters and offers a direction for future integration and optimization of multimodal social media data.

AirAsia has emerged as a dominant force among prominent low-cost airlines in recent years. However, the COVID-19 pandemic outbreak has severely impacted airline services, including AirAsia. There is a strong need for airline services to monitor customer experience and satisfaction from online customer reviews on the website to keep pace with changing customer perceptions toward their service quality. A growing number of travelers choose to express their experiences and emotions on online customer review platforms, resulting in substantial online airline service evaluations. The research analyzes 796 online customer reviews from Skytrax, a well-known online airline review website. The information hidden in customer-generated reviews is analyzed with the text mining technique, including topic modeling and sentiment analysis. The research uses the Latent Dirichlet Allocation (LDA) model for topic analysis and the Valence Aware Dictionary for Sentiment Reasoning (VADER) model for sentiment analysis. The sentiment ratio for AirAsia’s online reviews is approximately 59% positive and 41% negative. Only four reviews are neutral. The findings indicate that the online review of AirAsia has a greater proportion of positive sentiments than negative sentiments. In addition, the topic modeling shows hidden topics with the top high-probability keywords concerned with interior and seat, baggage, online service, staff service, flight schedule, and refund. The research demonstrates using sentiment analysis and topic modeling on customer review data as a more thorough alternative to survey-based models for researching airline service. The research contributes to the methodological advancements in text mining analysis and expands the current knowledge of customer review data.

With the development of social media platforms such as Weibo, they have provided a broad platform for the expression of public sentiments during the pandemic. This study aims to explore the emotional attitudes of Chinese netizens toward the COVID-19 opening-up policies and their related thematic characteristics. Using Python, 145,851 texts were collected from the Weibo platform. Sentiment analysis and topic modeling techniques were employed to reveal the distribution of public emotions and key themes. The study found that the proportions of emotions were as follows: Good (46%), Happy (11%), Anger (17%), Disgust (6%), Sadness (10%), Surprise (2%), and Fear (8%). Through topic analysis, the following main themes were identified: medical resource shortages, healthcare workers, national policies, and COVID-19 sequelae. Based on the results of sentiment and topic analysis, public emotions toward the COVID-19 opening-up policies were categorized into three dimensions: panic, trust, and acceptance. Panic was primarily associated with medical resource shortages, concerns about COVID-19 sequelae, and doubts about policy transparency and fairness. Trust was reflected in public gratitude toward healthcare workers and support for national policies. Acceptance represented the public's optimism about returning to normal life. The findings demonstrate that changes in public emotions not only reflect the social impact of policy implementation but also highlight the critical roles of medical resource allocation, information transparency, and psychological health support in adjusting pandemic policies. This study provides empirical evidence and theoretical support for the government to develop more precise pandemic control strategies.

BackgroundLoneliness is a global public health issue affecting a considerable number of people as well as burdening the public health system and increasing the risk of other life-threatening and life-damaging conditions. In USA an estimated 17% adults aged 18–70 report loneliness. The monetary loss as result of loneliness is estimated to be between USD 8074.80 and USD 12,0777.70 per person per year in the United Kingdom. But the dynamics of loneliness are not understood. Social media platforms have become a valuable source of data to study this phenomenon. ObjectivesThis paper aims to visualize the frequency of loneliness-related themes and topics in Twitter data. By using natural language (NLP) processing, sentiment analysis, and topic modeling, we seek to understand prevalent sentiments and concerns. Through interactive tree maps and radar plots, we present an engaging view of loneliness dimensions, allowing users to explore and gain insights into this issue on social media. We focus on comparative analysis of USA and India through analyzing tweets from both countries on loneliness. These two countries are the biggest countries population-wise where access to Twitter is legally allowed. MethodsThis study consists of two parts. In the first part, we employ NLP techniques and machine learning algorithms to extract and analyze tweets containing keywords related to loneliness. Through sentiment analysis and topic modeling, we discern linguistic patterns and contextual information to categorize the recurring themes and topics. Advanced text analytics is used to gain nuanced insights into the experiences, emotions, and challenges connected with loneliness. In the second part, interactive visualizations are developed to present the findings in an engaging and intuitive manner. Techniques such as tree maps and radar plots are utilized to transform the analyzed data into visually appealing representations. ResultsThe analysis of Twitter data yields valuable knowledge about the prevalence and nature of themes and topics associated with loneliness. The interactive visualizations present a comprehensive view of the sentiments and concerns expressed by Twitter users. These interactive plots provide a holistic view of the distribution of themes and topics associated with loneliness, allowing experts to explore and interact with the data, gaining deeper insights into the complexities surrounding this issue. ConclusionThis paper successfully explores themes and topics related to loneliness on Twitter by employing NLP, sentiment analysis, and topic modeling. The interactive visualizations enhance the accessibility and usability of the findings, providing valuable insights for various stakeholders. The study contributes to a deeper comprehension of loneliness in the context of social media.

In this study, we examine the Federal Reserve's communication strategies during the COVID‐19 pandemic, comparing them with communication during previous periods of economic stress. Using specialized dictionaries tailored to COVID‐19, unconventional monetary policy (UMP), and financial stability, combined with sentiment analysis and topic modeling techniques, we identify a distinct focus in Fed communication during the pandemic on financial stability, market volatility, social welfare, and UMP, characterized by notable contextual uncertainty. Through comparative analysis, we juxtapose the Fed's communication during the COVID‐19 crisis with its responses during the dot‐com and global financial crises, examining content, sentiment, and timing dimensions. Our findings reveal that Fed communication and policy actions were more reactive to the COVID‐19 crisis than to previous crises. Additionally, declining sentiment related to financial stability in interest rate announcements and minutes anticipated subsequent accommodative monetary policy decisions. We further document that communicating about UMP has become the “new normal” for the Fed's Federal Open Market Committee meeting minutes and Chairman's speeches since the Global Financial Crisis, reflecting an institutional adaptation in communication strategy following periods of economic distress. These findings contribute to our understanding of how central bank communication evolves during crises and how communication strategies adapt to exceptional economic circumstances.

Towards a Hybridized Business Intelligence Framework: A Sentiment Analysis and Topic Modeling Approach

Research indicates that adolescents who are vulnerable to self‐harm are likely to access social media specifically related to self‐harm, which often contains health myths and fails to provide linkages to appropriate resources. In this study, we examined the emotional discourse before and following a ban of graphic images of self‐harm on Facebook and Instagram. This observational case series study assesses a corpus of 8,013 tweets that specifically mention Facebook and/or Instagram between June 1, 2018, and May 31, 2019 (the ban occurred on February 7, 2019). Topical content was elucidated via statistical modeling to extract abstract topics in discourse. The graphic self‐harm ban produced emotional shifts in users of social media. A time series plot produced peaks of tweets on the banning of graphic images of self‐harm including political events, news coverage, and media exemplars of suicide (both celebrity and noncelebrity). We observed raw proportional differences with elevated emotions before the ban (i.e., disgust, joy, surprise, and trust) followed by elevated emotions occurring after the ban (i.e., anger, anticipation, and sadness). This study provides insight into how the policy‐change relating to self‐harm was received by those with a vested interest. These insights can be valuable to policymakers and advocates for those affected by self‐harm.

USA is among the countries that have been considerably affected by the COVID-19 to contain the largest proportion of cases globally. This research aims at investigating the American Community opinions polarity toward the outbreak of the virus in the US throughout twitter social media platform. Further, a topic modeling approach was employed to gain insights about the most discussed topic by the US community during pandemic. A total number of 1,385,469 tweets were collected for the purpose of the study over the period of early February to late April. In addition, unsupervised approaches were employed in the analysis including VADER lexicon for sentiment analysis and Latent Dirichlet Allocation (LDA) for topic modelling. The main findings of the research showed that the largest share of the collected tweets is of positive sentiment followed by negative and neutral. Further, temporal sentiment analysis on monthly basis in comparison with COVID-19 cases showed how the tweets polarity changed over time from early February to late April. In total, the polarity of the tweets was negative before the virus outbreak and positive during the outbreak. In addition, LDA analysis showed that the overall discussed topics tend are oriented toward economy, politics, and the spread of the virus outside the US in February while March and April topics are oriented toward discussing the prevention from the virus as well as spread of the virus inside USA.

Sentiment analysis has emerged as a powerful tool in financial markets, offering the ability to harness market participants' collective wisdom and emotions to make more informed investment decisions. This research paper presents a comprehensive literature review on sentiment analysis models in predicting stock prices, focusing on the Bank Nifty Index, a critical benchmark in the Indian financial sector. The review begins by elucidating the significance of sentiment analysis in stock price prediction, highlighting its pivotal role in capturing market sentiment, investor emotions, and their impact on asset valuations. A detailed overview of sentiment sources, encompassing financial news, social media, reports, and other relevant data streams, provides insight into the rich tapestry of information that sentiment analysis can leverage. The paper delves into various sentiment analysis approaches, from traditional lexicon-based and rule-based methods to cutting-edge machine learning and deep learning techniques. It elucidates the intricate process of integrating sentiment analysis with stock price prediction models, exploring feature concatenation, time series integration, and weighted data to incorporate sentiment insights effectively. Evaluation metrics crucial for assessing the performance of both sentiment analysis models and stock price prediction models are thoroughly discussed. It concludes by highlighting the substantial potential of sentiment analysis in augmenting stock price prediction, offering investors and traders valuable tools to navigate the complexities of financial markets.

Many higher education institutions in the world have switched to online learning due to the ongoing COVID-19 pandemic, which also has greatly contributed towards an increase of MOOCs enrollments across various disciplines. There are many factors that can influence the learning trajectory in MOOCs settings, and in order to gain a deeper understanding of learners' experience, we employ a quantitative research method, in which sentiment analysis and topic modeling are applied. In this perspective, learners' reviews from the learning platform Coursera are examined to identify the main topics associated with the course and the learners' attitudes and opinions towards these topics. For this purpose, a total of 28,281 reviews scraped from five courses within the field of data science are analyzed, and consequently nine course topics for which learners have commented on are found. The identified topics include: content, delivery, assessment, learning experience, tools, video material, teaching style, instructor skills and course provider. Next, each topic is assigned a sentiment score using a lexicon-based approach, and the topics which mostly affect the learning experience are finally determined and discussed.

Understanding and comprehending humans' views, beliefs, attitudes, or opinions toward a particular entity is sentiment analysis (SA). Advancements in e‐commerce platforms has led to an abundance of the real‐time and free forms of opinions floating on social media platforms. This real‐world data are imprecise and vague hence fuzzy logic is required to deal with such subjective data. Since opinions can be fuzzy in nature and definitions of opinion words can be elucidated differently; fuzzy logic has witnessed itself as an effective method to capture the expression of opinions. The study presents an elaborate review of the around 170 published research works for SA using fuzzy logic. The primary emphasis is focused on text‐based SA, audio‐based SA, and fusion of text‐audio features‐based SA. This article discusses the various novel ways of classifying fuzzy logic‐based SA research articles, which have not been accomplished by any other review article till date. The article puts forward the importance of SA tasks and identifies how fuzzy logic adds to this importance. Finally, the article outlines a taxonomy for sentiment classification based on the technique‐supervised and unsupervised in the SA models and comprehensively reviews the SA approaches specific to their task. Prominently, this study highlights the suitability of fuzzy‐based SA approaches into five different classes vis‐a‐vis (a) Sentiment Cognition from Words using fuzzy logic, (b) Sentiment Cognition from Phrases using fuzzy logic, (c) Fuzzy‐rule based SA, (d) Neuro‐fuzzy network‐based SA, and (e) Fuzzy Emotion Recognition.This article is categorized under:Algorithmic Development > Text MiningFundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

The Covid-19 pandemic has disrupted the world economy and significantly influenced the tourism industry. Millions of people have shared their emotions, views, facts, and circumstances on numerous social media platforms, which has resulted in a massive flow of information. The high-density social media data has drawn many researchers to extract valuable information and understand the user’s emotions during the pandemic time. The research looks at the data collected from the micro-blogging site Twitter for the tourism sector, emphasizing sub-domains hospitality and healthcare. The sentiment of approximately 20,000 tweets have been calculated using Valence Aware Dictionary for Sentiment Reasoning (VADER) model. Furthermore, topic modeling was used to reveal certain hidden themes and determine the narrative and direction of the topics related to tourism healthcare, and hospitality. Topic modeling also helped us to identify inter-cluster similar terms and analyzing the flow of information from a group of a similar opinion. Finally, a cutting-edge deep learning classification model was used with different epoch sizes of the dataset to anticipate and classify the people’s feelings. The deep learning model has been tested with multiple parameters such as training set accuracy, test set accuracy, validation loss, validation accuracy, etc., and resulted in more than a 90% in training set accuracy tourism hospitality and healthcare reported 80.9 and 78.7% respectively on test set accuracy.

Social media has been a real-world sensor to observe the pulse of society. Although it provides unique communication opportunities, it also brings along vital challenges. One of them is hate speech, which attacks a single individual or targeted groups. Previous researchers claim that among social networks, the Twitter platform is mostly used to spread hate speech. However, data on a larger scale makes it hard to capture and understand the nature of hate speech on Twitter within specific food brands. In this study, sentiment analysis techniques were used to filter hate speech on Twitter and on three popular food brands in Malaysia. This study was conducted in five phases, namely raw data collection, pre-processing, sentiment analysis, visualization, and performance evaluation. This corpus consists of 28,898 data samples based on user tweet searches. A Twitter API was created and SQLite was used to store all the sample data. VADER sentiment analysis is used to classify tweet sentiment into positive, negative, and neutral. In the visualization phase, these three food brands are visualized using a histogram to gain sentiment analysis insights. Then, three machine learning methods were implemented to predict the best model for sentiment analysis. The Decision Tree classifier outperforms the average accuracy in Support Vector Machine and Logistic Regression with 99.99% for the training data set. This study provides insights to assist humans in making decisions. With the growth of opinions expressed in multimedia on social media, such as spoken feedback on Twitter, sentiment analysis has the potential to become a more news aggregation and low-cost endeavour.

Public Perceptions of EV Charging Infrastructure: A Combined Sentiment Analysis and Topic Modeling Approach

Automated sentiment analysis is becoming increasingly recognized due to the growing importance of social media and e-commerce platform review websites. Deep neural networks outperform traditional lexicon-based and machine learning methods by effectively exploiting contextual word embeddings to generate dense document representation. However, this representation model is not fully adequate to capture topical semantics and the sentiment polarity of words. To overcome these problems, a novel sentiment analysis model is proposed that utilizes richer document representations of word-emotion associations and topic models, which is the main computational novelty of this study. The sentiment analysis model integrates word embeddings with lexicon-based sentiment and emotion indicators, including negations and emoticons, and to further improve its performance, a topic modeling component is utilized together with a bag-of-words model based on a supervised term weighting scheme. The effectiveness of the proposed model is evaluated using large datasets of Amazon product reviews and hotel reviews. Experimental results prove that the proposed document representation is valid for the sentiment analysis of product and hotel reviews, irrespective of their class imbalance. The results also show that the proposed model improves on existing machine learning methods.