Investigating public perceptions regarding the Long COVID on Twitter using sentiment analysis and topic modeling

Background:An estimated 10 to 30 percent of people who become infected with Severe acute respiratory syndrome coronavirus 2 will experience persistent symptoms after recovering from Coronavirus Disease 2019(COVID-19),which is known as Long COVID.Social media platforms like Facebook and Twitter are the primary sources to gather and examine people’s opinion and sentiments towards various topics.Methods:In this paper,we aimed to examine sentiments,discover key themes and associated topics in Long COVID-related messages posted by Twitter users in the US between March 2022 and April 2022 using sentiment analysis and topic modeling.Results:A total of 117,789 tweets were examined,of which three dominant themes were identified,ranging from symptoms to social and economic impacts,and preventive measures.We also found that more negative sentiments were expressed in the tweets by users toward long-term COVID-19.Conclusions:Our research throws light on dominant themes,topics and sentiments surrounding the ongoing public health crisis.From the insights gained,we discuss the major implications of this study for health practitioners and policymakers.

Topic Sentiment Analysis in Online Learning Community from College Students

Purpose:Opinion mining and sentiment analysis in Online Learning Community can truly reflect the students’learning situation,which provides the necessary theoretical basis for following revision of teaching plans.To improve the accuracy of topic-sentiment analysis,a novel model for topic sentiment analysis is proposed that outperforms other state-of-art models.Methodology/approach:We aim at highlighting the identification and visualization of topic sentiment based on learning topic mining and sentiment clustering at various granularitylevels.The proposed method comprised data preprocessing,topic detection,sentiment analysis,and visualization.Findings:The proposed model can effectively perceive students’sentiment tendencies on different topics,which provides powerful practical reference for improving the quality of information services in teaching practice.Research limitations:The model obtains the topic-terminology hybrid matrix and the document-topic hybrid matrix by selecting the real user’s comment information on the basis of LDA topic detection approach,without considering the intensity of students’sentiments and their evolutionary trends.Practical implications:The implication and association rules to visualize the negative sentiment in comments or reviews enable teachers and administrators to access a certain plaint,which can be utilized as a reference for enhancing the accuracy of learning content recommendation,and evaluating the quality of their services.Originality/value:The topic-sentiment analysis model can clarify the hierarchical dependencies between different topics,which lay the foundation for improving the accuracy of teaching content recommendation and optimizing the knowledge coherence of related courses.

Sustainable strategy for corporate governance based on the sentiment analysis of financial reports with CSR

Focusing only on shareholders’financial return is not consistent with the concept of sustainable corporate governance.In contrast to financial performance,corporate social responsibility(CSR)is a non-financial performance index.Financial reports consist of both financial and non-financial disclosures.These disclosures help investors make decisions.This paper characterizes the interaction between the sentiment analysis of financial reports and CSR scores.The classification accuracy through SVM exceeds 86%.The empirical study shows that the financial report sentiment based on the PESTEL model,Porter’s Five Forces model,and Value Chain(Primary and Support Activities)significantly correlates to the CSR score.

Public Opinions on ChatGPT:An Analysis of Reddit Discussions by Using Sentiment Analysis,Topic Modeling,and SWOT Analysis

The sudden arrival of AI(Artificial Intelligence) into people's daily lives all around the world was marked by the introduction of ChatGPT, which was officially released on November 30, 2022. This AI invasion in our lives drew the attention of not only tech enthusiasts but also scholars from diverse fields, as its capacity extends across various fields. Consequently, numerous articles and journals have been discussing ChatGPT, making it a headline for several topics. However, it does not reflect most public opinion about the product. Therefore, this paper investigated the public's opinions on ChatGPT through topic modelling, Vader-based sentiment analysis and SWOT analysis. To gather data for this study, 202905 comments from the Reddit platform were collected between December 2022 and December 2023. The findings reveal that the Reddit community engaged in discussions related to ChatGPT, covering a range of topics including comparisons with traditional search engines, the impacts on software development, job market, and education industry, exploring ChatGPT's responses on entertainment and politics, the responses from Dan, the alter ego of ChatGPT, the ethical usage of user data as well as queries related to the AI-generated images. The sentiment analysis indicates that most people hold positive views towards this innovative technology across these several aspects. However, concerns also arise regarding the potential negative impacts associated with this product. The SWOT analysis of these results highlights both the strengths and pain points, market opportunities and threats associated with ChatGPT. This analysis also serves as a foundation for providing recommendations aimed at the product development and policy implementation in this paper.

A Semi-Supervised Topic Model Incorporating Sentiment and Dynamic Characteristic

With the rapid popularization of social applications, various kinds of social media have developed into an important platform for publishing information and expressing opinion. Detecting hidden topics from the huge amount of user-generated contents is of great commerce value and social significance. However traditional text analysis approachesonly focus on the statistical correlation between words, but ignore the sentiment tendency and the temporal properties which may have great effects on topic detection results. This paper proposed a Dynamic Sentiment-Topic(DST) model which can not only detect and track the dynamic topics but also analyze the shift of public's sentiment tendency towards certain topic.Expectation-Maximization algorithm was used in DST model to estimate the latent distribution, and we used Gibbs sampling method to sample new document set and update the hyper parameters and distributions.Experiments are conducted on a real dataset and the results show that DST model outperforms the existing algorithms in terms of topic detection and sentiment accuracy.

Topic Modelling and Sentimental Analysis of Students’Reviews

Globally,educational institutions have reported a dramatic shift to online learning in an effort to contain the COVID-19 pandemic.The fundamental concern has been the continuance of education.As a result,several novel solutions have been developed to address technical and pedagogical issues.However,these were not the only difficulties that students faced.The implemented solutions involved the operation of the educational process with less regard for students’changing circumstances,which obliged them to study from home.Students should be asked to provide a full list of their concerns.As a result,student reflections,including those from Saudi Arabia,have been analysed to identify obstacles encountered during the COVID-19 pandemic.However,most of the analyses relied on closed-ended questions,which limited student involvement.To delve into students’responses,this study used open-ended questions,a qualitative method(content analysis),a quantitative method(topic modelling),and a sentimental analysis.This study also looked at students’emotional states during and after the COVID-19 pandemic.In terms of determining trends in students’input,the results showed that quantitative and qualitative methods produced similar outcomes.Students had unfavourable sentiments about studying during COVID-19 and positive sentiments about the face-to-face study.Furthermore,topic modelling has revealed that the majority of difficulties are more related to the environment(home)and social life.Students were less accepting of online learning.As a result,it is possible to conclude that face-to-face study still attracts students and provides benefits that online study cannot,such as social interaction and effective eye-to-eye communication.

Topic features for machine learning-based sentiment analysis in Indonesian tweets

Purpose–The purpose of this paper is to analyze topics as alternative features for sentiment analysis in Indonesian tweets.Design/methodology/approach–Given Indonesian tweets,the processes of sentiment analysis start by extracting features from the tweets.The features are words or topics.The authors use non-negative matrix factorization to extract the topics and apply a support vector machine to classify the tweets into its sentiment class.Findings–The authors analyze the accuracy using the two-class and three-class sentiment analysis data sets.Both data sets are about sentiments of candidates for Indonesian presidential election.The experiments show that the standard word features give better accuracies than the topics features for the two-class sentiment analysis.Moreover,the topic features can slightly improve the accuracy of the standard word features.The topic features can also improve the accuracy of the standard word features for the three-class sentiment analysis.Originality/value–The standard textual data representation for sentiment analysis using machine learning is bag of word and its extensions mainly created by natural language processing.This paper applies topics as novel features for the machine learning-based sentiment analysis in Indonesian tweets.

Context-Aware Social Media User Sentiment Analysis

The user-generated social media messages usually contain considerable multimodal content.Such messages are usually short and lack explicit sentiment words.However,we can understand the sentiment associated with such messages by analyzing the context,which is essential to improve the sentiment analysis performance.Unfortunately,majority of the existing studies consider the impact of contextual information based on a single data model.In this study,we propose a novel model for performing context-aware user sentiment analysis.This model involves the semantic correlation of different modalities and the effects of tweet context information.Based on our experimental results obtained using the Twitter dataset,our approach is observed to outperform the other existing methods in analysing user sentiment.

An analysis of ridesharing trip time using advanced text mining techniques

The time cost of ridesharing rental represents a crucial factor influencing users'decisions to rent a car.Researchers have explored this aspect through text analysis and questionnaires.However,the current research faces limitations in terms of data quantity and analysis methods,preventing the extraction of key information.Therefore,there is a need to further optimize the level of public opinion analysis.This study aimed to investigate user perspectives concerning travel time in ridesharing,both pre and post-pandemic,within the Twitter application.Our analysis focused on a dataset from users residing in the USA and India,with considerations for demographic variables such as age and gender.To accomplish our research objectives,we employed Latent Dirichlet Allocation for topic modeling and BERT for sentiment analysis.Our findings revealed significant influences of the pandemic and the user's country of origin on sentiment.Notably,there was a discernible increase in positive sentiment among users from both countries following the pandemic,particularly among older individuals.These findings bear relevance to the ridesharing industry,offering insights that can aid in establishing benchmarks for improving travel time.Such improvements are instrumental in enabling ridesharing companies to effectively compete with other public transportation alternatives.

基于LDA主题模型的“双一流”高校图书馆用户评论文本数据挖掘

[目的/意义]图书馆用户评论中包含用户对图书馆服务和管理意见的重要信息,对用户评论文本进行深度挖掘,旨在探究用户关心主题及其情感态度、隐含诉求,为高校图书馆建设提供理论依据和数据支持。[方法/过程]以“双一流”高校图书馆为研究对象,收集用户在大众点评网上的评论数据,通过LDA主题建模,得到当前用户评论主要集中在信息资源建设、娱乐休闲服务、馆内设施环境三个维度,进而分别分析三个维度下的关键词共现网络和用户情感态度。[结果/结论]用户对高校图书馆的总体情感态度是积极正向的,但在纸质资源管理、社会化服务、馆内人性化服务等方面表现出负面情绪。

突发公共卫生事件对消费者在线评论情感倾向的影响研究

基于情感信息理论,以去哪儿网北京市酒店信息和评论数据为样本,探讨突发公共卫生事件对消费者评论情感倾向的影响,以及消费者洞察力水平与确定性水平的调节作用。研究表明,突发公共卫生事件降低了消费者评论的积极情感倾向,并提升了其消极情感倾向;消费者的洞察力水平与确定性水平会削弱突发公共卫生事件对消费者评论积极情感倾向的负向影响,但未显著调节消费者评论消极情感倾向所受影响。运用主题情感分析方法,对消费者评论情感倾向在不同时期内分布的异同进一步比较分析发现,突发公共卫生事件发生后,消费者对酒店体验方面的积极感知提升,而对酒店硬件设施方面的不满加剧。

基于知乎平台内容挖掘的元宇宙公众感知研究

[目的/意义]通过分析公众对元宇宙中个体、业界、政府3方面内容的感知,针对性地为个体、企业、政府参与元宇宙生态建设提供建议。[方法/过程]参考三螺旋模型,构建基于个体—业界—政府维度的元宇宙公众感知分析框架,采用改进LDA和BERT算法,基于知乎问答分析公众对元宇宙中个体、业界、政府3方面内容的感知主题,主题的热度、情感及相关演化。[结果/结论]公众在个体、业界、政府维度上的感知既有差异又有联系。演化上,公众关注点逐渐细化深入,态度受体验、舆论影响显著,但趋于理性。通过挖掘不同维度的热点、潜力点及问题,能针对性地为不同主体提供建议。

基于文本挖掘的新冠肺炎疫情下医药在线消费者的需求研究

基于新冠肺炎疫情下医药电商交易规模的爆炸式增长,对医药电商在线评论进行文本分析,以某B2C医药电商平台2019—2021年在线评论数据为样本,利用LDA主题模型提取在线评论蕴含的主题,并构建情感词典融合深度学习的情感分析模型,对评论和主题词进行情感分析。研究结果显示:1)消费者网购医药商品始终关注平台的可靠性、物流服务、商品价格、药品的使用效果;2)新冠肺炎疫情爆发之前,消费者对服务态度、商品品牌、购买便捷性有很大关注度;疫情爆发后对感冒类和维生素类药品关注度更高,疫情的爆发会影响消费者的购药决策;后疫情时代,消费者更关注商品性价比、购买快捷性以及药品的品质;3)消费者对于在医药电商平台进行购药整体上表现出积极正面的情感态度;4)负面在线评论主要集中在价格、药效、处方药购买、虚假宣传、物流包装、限购等方面。本研究挖掘出疫情下消费者对于网购医药商品的需求重点和痛点,对医药电商平台改善服务质量提供建设性意见。

分布式光伏开发公众评论的情感倾向及引导策略

整县推进屋顶分布式光伏开发试点工作的实施,推动了分布式光伏的快速发展,也引发了公众在社交媒体上的热烈讨论,其中不乏有大量的负面评论。这些评论一方面映射了分布式光伏开发实际操作中存在的乱象和问题,另一方面也使公众对分布式光伏开发产生了极大的误解和偏见,进而阻碍了分布式光伏开发的进程。当前对分布式光伏开发公众认知和采纳意愿的研究以问卷调查为主,缺乏基于评论数据的研究。以整县推进屋顶分布式光伏开发为背景,基于抖音评论数据,利用情感分析与BERTopic主题建模方法,探讨了公众对分布式光伏开发的情感倾向及主题特征。研究结果表明,公众评论以负面为主,在时间上波动较大,且与媒体负面报道有较大的关联性,评论数量在空间上呈集中分布态势,与试点县域数量、分布式光伏开发进度密切相关;公众正面情绪主要源于国家推广力度及政府监管强度,负面情绪主要源于公众对“光伏骗局”的担忧。结合以上结果,从政府监管、舆论引导、后期保障三个方面提出了屋顶分布式光伏发展的政策建议。

基于社交媒体文本挖掘的居民低碳出行意向分析

城市交通运输是碳减排的重要领域,其中城市居民出行碳排放占比达到了20%,低碳出行对缓解全球气候变化具有重要意义。了解居民对低碳出行的意向有助于推广该行为,社交媒体平台提供了大量有价值的信息,文章基于新浪微博中的低碳出行博文数据,采用BERT-BiLSTM模型、LDA主题模型的文本挖掘方法分析居民对低碳出行的行为意向和关注主题。结果表明:居民对低碳出行整体上持积极意向;地铁和公交最受欢迎;低碳出行意向是不同因素综合作用的结果;明星效应对低碳出行意向影响显著。研究结论将有助于低碳出行政策的完善。

“一带一路”倡议的多维海外认知对比研究:基于欧洲智库文本的分析

2023年是“一带一路”倡议提出10周年。10年来,共建“一带一路”倡议从理念到行动,从愿景到现实,已成为推动构建人类命运共同体的重要实践平台。对欧洲智库报告文本进行分析研判可以管窥欧洲对“一带一路”建设实施10年来的整体认知,对推进“一带一路”倡议下一阶段发展具有重要意义。通过对宾夕法尼亚大学(TTCSP)和Bruegel智库联合发布的《2020全球智库指数报告》中的212家欧洲智库在2013年前后至2022年9月期间公开发表的“一带一路”相关的13539份文本进行文本挖掘,探讨欧洲一流智库关于“一带一路”倡议的情感倾向和议程设置,从一个侧面观察“旋转门”机制下欧洲公共决策和舆论层的观点与立场,在总结此倡议在欧洲推进过程中所面临的潜在机遇和挑战的同时,为下一阶段“一带一路”建设中如何有效改善此倡议在海外公共舆论环境中的形象提供数据支持与参考。研究发现,欧洲智库对“一带一路”倡议整体上持中立略偏正面的态度,但不同区域和国家也存在明显差异。中东欧国家智库的看法较为正面,北欧及波罗的海国家智库则有较为明显的负面情绪,而西欧与南欧国家智库比较中立。主题建模进一步发现,由经济因素支撑的“共谋发展”论调在欧洲主流公共舆论圈具有较强的代表性。

视频弹幕评论舆情分析系统设计与实现

随着用户生成内容视频网站的普及,弹幕评论成为用户表达情感性意见的重要方式。弹幕评论的即时性和互动性为网络舆情分析提供了丰富的数据资源,但其匿名性和短文本的特点给数据分析带来一定的困难。设计视频弹幕评论网络舆情分析系统,能够有效识别和过滤异常用户,并进行情感分析,最终完成对弹幕评论的主题聚类和可视化展示。系统采用前后端分离架构,前端基于Vue框架,后端使用Django框架,数据存储采用MySQL数据库。通过对Bilibili视频网站弹幕数据的实验分析,验证了系统的有效性和稳定性,促进了网络舆情分析系统的发展。

基于LDA主题模型的社交媒体隐私政策合规性评价研究

[目的/意义]在个人信息保护日渐重要的今天,开展我国社交媒体隐私政策合规性评价研究,可为完善社交媒体隐私政策和加强个人信息保护提供参考。[方法/过程]文章选取28个常用社交媒体,基于LDA主题模型、完整性评价和阅读感分析对其隐私政策文本进行比较分析。[结果/结论]研究发现,随着相关信息保护法的出台,社交媒体在隐私保护、信息安全等方面已取得了积极进步,但在政策完整性、特殊群体的保护和可读性方面仍有进一步完善的空间。未来,可从法制建设和用户权利保障、特殊群体保护以及文本可读性3个方面进行完善。

混合性焦虑抑郁障碍服务质量情感主题识别研究

[目的/意义]为识别在线医疗社区中混合性焦虑抑郁障碍患者评论医疗服务质量情感及主题,提出一种基于CNN-BiLSTM和LDA模型的服务质量情感主题识别模型。[方法/过程]首先,构建CNN-BiLSTM模型提取患者评论内外关键特征得到情感倾向分布;其次,运用LDA主题模型提取患者正负向评论主题,结合《医院评价标准(征求意见稿)》得到医疗服务质量主题,从分布和情感词对正负向服务质量进行挖掘。[结果/结论]CNN-BiLSTM的F1值为94.43%,均优于其他对比模型;结合LDA主题模型和相关文献得到5维医疗服务质量主题及分布;根据主题情感词及分布得到负向评论产生的主要原因,为识别和改善医疗服务质量提供有效决策支持。

“一带一路”议题全球舆论话语图景与中国应对--基于2013-2023年全球社交媒体平台X的大数据研究

[研究目的]国际社交媒体反映全球民间舆论。通过分析X(原Twitter)平台上“一带一路”相关推文,了解国际舆论对该议题的态度和反应,以提出中国应对策略。[研究方法]基于数字媒体分析方法,对近十年的相关推文进行情感分析、主题分析和社会网络分析。[研究结论]发现X平台关于“一带一路”议题的舆情的三大现状,即;关注度高,受主要相关事件影响;情感波动显著,西方主流媒体叠加负面议题;中、英文推文场域相互溢出,中国主流媒体舆论引导力不足。基于此提出三个对策,即:强化舆情风险预测,提前制定应对方案;及时回应外部关切,强化沟通与危机管理;积极设置话题,强化舆论引导与议题塑造。