COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining

With the spread and development of new epidemics,it is of great reference value to identify the changing trends of epidemics in public emotions.We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining.A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed.Establish a“Scrapy-Redis-Bloomfilter”distributed crawler framework to collect data.The system can judge the positive and negative emotions of the reviewer based on the comments,and can also reflect the depth of the seven emotions such as Hopeful,Happy,and Depressed.Finally,we improved the sentiment discriminant model of this system and compared the sentiment discriminant error of COVID-19 related comments with the Jiagu deep learning model.The results show that our model has better generalization ability and smaller discriminant error.We designed a large data visualization screen,which can clearly show the trend of public emotions,the proportion of various emotion categories,keywords,hot topics,etc.,and fully and intuitively reflect the development of public opinion.

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

Design of Automated Opinion Mining Model Using Optimized Fuzzy Neural Network

Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recentyears. Likewise, Machine Learning (ML) approaches is one of the interestingresearch domains that are highly helpful and are increasingly applied in severalbusiness domains. In this background, the current research paper focuses onthe design of automated opinion mining model using Deer Hunting Optimization Algorithm (DHOA) with Fuzzy Neural Network (FNN) abbreviatedas DHOA-FNN model. The proposed DHOA-FNN technique involves fourdifferent stages namely, preprocessing, feature extraction, classification, andparameter tuning. In addition to the above, the proposed DHOA-FNN modelhas two stages of feature extraction namely, Glove and N-gram approach.Moreover, FNN model is utilized as a classification model whereas GTOA isused for the optimization of parameters. The novelty of current work is thatthe GTOA is designed to tune the parameters of FNN model. An extensiverange of simulations was carried out on the benchmark dataset and the resultswere examined under diverse measures. The experimental results highlightedthe promising performance of DHOA-FNN model over recent state-of-the-arttechniques with a maximum accuracy of 0.9928.

An Improved Method for Extractive Based Opinion Summarization Using Opinion Mining

Opinion summarization recapitulates the opinions about a common topic automatically.The primary motive of summarization is to preserve the properties of the text and is shortened in a way with no loss in the semantics of the text.The need of automatic summarization efficiently resulted in increased interest among communities of Natural Language Processing and Text Mining.This paper emphasis on building an extractive summarization system combining the features of principal component analysis for dimensionality reduction and bidirectional Recurrent Neural Networks and Long Short-Term Memory(RNN-LSTM)deep learning model for short and exact synopsis using seq2seq model.It presents a paradigm shift with regard to the way extractive summaries are generated.Novel algorithms for word extraction using assertions are proposed.The semantic framework is well-grounded in this research facilitating the correct decision making process after reviewing huge amount of online reviews,considering all its important features into account.The advantages of the proposed solution provides greater computational efficiency,better inferences from social media,data understanding,robustness and handling sparse data.Experiments on the different datasets also outperforms the previous researches and the accuracy is claimed to achieve more than the baselines,showing the efficiency and the novelty in the research paper.The comparisons are done by calculating accuracy with different baselines using Rouge tool.

Swarming Behavior of Harris Hawks Optimizer for Arabic Opinion Mining

At present,the immense development of social networks allows generating a significant amount of textual data,which has facilitated researchers to explore the field of opinion mining.In addition,the processing of textual opinions based on the term frequency-inverse document frequency method gives rise to a dimensionality problem.This study aims to detect the nature of opinions in the Arabic language employing a swarm intelligence(SI)-based algorithm,Harris hawks algorithm,to select the most relevant terms.The experimental study has been tested on two datasets:Arabic Jordanian General Tweets and Opinion Corpus for Arabic.In terms of accuracy and number of features,the results are better than those of other SI based algorithms,such as grey wolf optimizer and grasshopper optimization algorithm,and other algorithms in the literature,such as differential evolution,genetic algorithm,particle swarm optimization,basic and enhanced whale optimizer algorithm,slap swarm algorithm,and ant–lion optimizer.

Aspect-Level Opinion Mining of Online Customer Reviews

This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and aspect-dependent sentiment lexicons from online customer reviews. An aspect-dependent sentiment lexicon refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities with respect to a specific aspect. We then apply the extracted aspectdependent sentiment lexicons to a series of aspect-level opinion mining tasks, including implicit aspect identification, aspect-based extractive opinion summarization, and aspect-level sentiment classification. Experimental results demonstrate the effectiveness of the JAS model in learning aspectdependent sentiment lexicons and the practical values of the extracted lexicons when applied to these practical tasks.

Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

Opinion Mining(OM)studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide.Though the interest in OM studies in the Arabic language is growing among researchers,it needs a vast number of investigations due to the unique morphological principles of the language.Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features.The comparative OM studies in the English language are wide and novel.But,comparative OM studies in the Arabic language are yet to be established and are still in a nascent stage.The unique features of the Arabic language make it essential to expand the studies regarding the Arabic text.It contains unique featuressuchasdiacritics,elongation,inflectionandwordlength.Thecurrent study proposes a Political Optimizer with Probabilistic Neural Network-based Comparative Opinion Mining(POPNN-COM)model for the Arabic text.The proposed POPNN-COM model aims to recognize comparative and non-comparative texts in Arabic in the context of social media.Initially,the POPNN-COM model involves different levels of data pre-processing to transform the input data into a useful format.Then,the pre-processed data is fed into the PNN model for classification and recognition of the data under different class labels.At last,the PO algorithm is employed for fine-tuning the parameters involved in this model to achieve enhanced results.The proposed POPNN-COM model was experimentally validated using two standard datasets,and the outcomes established the promising performance of the proposed POPNN-COM method over other recent approaches.

Towards Mining Public Opinion: An Attention-Based Long Short Term Memory Network Using Transfer Learning

The Internet provides a large number of tools and resources, such as social media sites, online newsgroups, blogs, electronic forums, virtual communities, and online travel sites, for consumers to express their views or opinions regarding various issues. These opinions can help organizations like tourism to improve their products and services for their consumers. Opinion mining refers to a process of identifying emotions by applying Natural Language Processing (NLP) techniques to a pool of texts. This paper mainly focuses on mining public opinion from the hotel reviews domain. To do so, we proposed a novel technique called the Attention-Based Long Short Term Memory (Attention-LSTM) Network using a transfer learning approach. We empirically analyzed several machine learning and deep learning methods and observed our proposed technique provided an adequate performance for mining public opinion in the hotel reviews domain.

An Opinion Mining Task in Turkish Language： A Model for Assigning Opinions in Turkish Blogs to the Polarities

Global changes took place at a neck-breaking speed in lots of fields along with the Web 2.0 era, which can be stated as the new Internet trend. Web pages which once were a statical structure that can be said to become dynamic pages created by users, and in this regard they can be said to have been democratized by evolving. Social media, which were structured alongside with this era, by providing a large data flow for businesses, present new and improvable opportunities in the field of creating effective strategies. There are lots of blogs in today＇s Internet environment which includes customer ideas regarding the products/services that they possess. This environment, which in a way globalizes the customer ideas, is a new medium suitable for examination in terms of its increasing the business-customer interaction and due to its transporter nature; it provides the text data that may be analyzed in the field of Customer Relationship Management to businesses. Thus, businesses should follow blog environments to see how the product/service they provide is greeted in terms of the customer focus and it should be seen as an important job on which they can conduct effective analyses. For this purpose, a model proposal that will assign the ideas to the Turkish blogs was given in the study. Opinion mining methods were used in the model, and so to perceive a general look-on about products/services, a methodology was devised, which will assign the text based opinion data on the Turkish blogs to the poles. Success of the pole assignment of the model is evaluated with the precision measure.

Blog Opinion Retrieval with Generation Model and Mixture Model

Blog opinion retrieval aims to find blogs with opinionated information related to a given topic.Its main problem is to compute the opinion score,which balances topic relevance and opinion relevance.To deal with this problem a generative model deduced by a Bayesian approach is pro-posed,and an improved mixture model is proposed to estimate the opinion relevance between a blog and a given topic in our retrieval framework.Moreover,pointwise mutual information is used to expand sentiment words for different topics based on a general sentimental lexicon.The correlation between topic and candidate words is applied in the process of both expanding sentiment words and estimating sentence opinion scores.Experimental results show that the proposed approaches improve upon the state-of-the-art opinion retrieval method on TREC2010 dataset.

“If We Only Knew How You Feel”—A Comparative Study of Automated vs. Manual Classification of Opinions of Customers on Digital Media

The Web development has drastically changed the human interaction and communication, leading to an exponential growth of data generated by users in various digital media. This mass of data provides opportunities for understanding people’s opinions about products, services, processes, events, political movements, and organizational strategies. In this context, it becomes important for companies to be able to assess customer satisfaction about their products or services. One of the ways to evaluate customer sentiment is the use of Sentiment Analysis, also known as Opinion Mining. This research aims to compare the efficiency of an automatic classifier based on dictionary with the classification by human jurors in a set of comments made by customers in Portuguese language. The data consist of opinions of service users of one of the largest Brazilian online employment agencies. The performance evaluation of the classification models was done using Kappa index and a Confusion Matrix. As the main finding, it is noteworthy that the agreement between the classifier and the human jurors came to moderate, with better performance for the dictionary-based classifier. This result was considered satisfactory, considering that the Sentiment Analysis in Portuguese language is a complex task and demands more research and development.

A Statistical Analysis of Reliability of Audit Opinions as Bankruptcy Predictors

This research measures the reliability of audit firms in predicting bankruptcy for United States （US） listed financial institutions. The object of analysis is the going concern opinion （GCO）, widely considered as a bankruptcy warning signal to stakeholders. The sample is composed of 42 US listed financial companies that filed for Chapter 11 between 1998 and 2011. To highlight the differences between bankrupting and healthy firms, a matching sample composed of 42 randomly picked healthy US listed financial companies is collected. We concentrate on financial institutions, whereas the existing literature pays considerably greater attention to the industrial sector. This research imbalance is remarkable and particularly unexpected in the wake of recent financial scandals. Literature points out two main approaches on bankruptcy prediction： （1） purely mathematical; and （2） approaches based on a combination of auditor knowledge, expertise, and experience. The use of data mining techniques allows us to benefit from the best features of both approaches. Statistical tools used in the analysis are： Logit regression, support vector machines （SVMs）, and an AdaBoost meta-algorithm. Findings show a quite low reliability of GCOs in predicting bankruptcy. It is likely that auditors consider further information in supporting their audit opinions, aside from financial-economic ratios. The scant predictive ability of auditors might be due to critical relationships with distressed clients, as suggested by recent literature.

Social Opinion Network Analytics in Community Based Customer Churn Prediction

Community based churn prediction,or the assignment of recognising the influence of a customer’s community in churn prediction has become an important concern for firms in many different industries.While churn prediction until recent times have focused only on transactional dataset(targeted approach),the untargeted approach through product advisement,digital marketing and expressions in customer’s opinion on the social media like Twitter,have not been fully harnessed.Although this data source has become an important influencing factor with lasting impact on churn management.Since Social Network Analysis(SNA)has become a blended approach for churn prediction and management in modern era,customers residing online predominantly and collectively decide and determines the momentum of churn prediction,retention and decision support.In existing SNA approaches,customers are classified as churner or non-churner(1 or 0).Oftentimes,the customer’s opinion is also neglected and the network structure of community members are not exploited.Consequently,the pattern and influential abilities of customers’opinion on relative members of the community are not analysed.Thus,the research developed a Churn Service Information Graph(CSIG)to define a quadruple churn category(churner,potential churner,inertia customer,premium customer)for non-opinionated customers via the power of relative affinity around opinionated customers on a direct node to node SNA.The essence is to use data mining technique to investigate the patterns of opinion between people in a network or group.Consequently,every member of the online social network community is dynamically classified into a churn category for an improved targeted customer acquisition,retention and/or decision supports in churn management.

负面评论引导的高维多元数据可视分析系统

随着互联网平台以及多用户社交网络的成熟,群体用户消费体验的参考价值日趋扩大,在海量评论数据中,负面评论对企业和消费者的参考价值更为突出,有效的面向负面评论的可视分析是有必要的.针对评论数据高维多元的特征,为了给企业和消费者提供全新的评论分析视角,以负面评论为切入点,给出负面评论的划定范围,提出了一个交互式的可视分析系统.首先,利用情感分析和意见挖掘方法处理用户评论数据,并提出评论个体影响力差异量化方法;其次设计了主题情感波纹图、评论比较视图等一系列交互式可视化表示方法,利用动态交互式方法构建多维度关联视图探索影响负面评论产生的因素,负面评论产生的原因及其个体化差异.3个案例的结果表明,所提系统是有效和实用的;同时,该系统还可扩展应用于其他领域的评论文本可视分析中.

基于在线品牌社区意见领袖的用户关键需求挖掘

随着社会的快速发展以及技术的不断进步,人们生活节奏不断加快,对产品的需求也在快速发生着变化。在线评论是目前用户需求表达的重要渠道。为克服不加区分挖掘所有评论的传统用户需求挖掘方法耗时过长,难以聚焦用户关键需求的问题,从用户关键性与需求关键性的双关键性角度出发,研发了一种基于在线品牌社区意见领袖的用户关键需求挖掘方法以快速获取用户重要需求,简称KEY-DEMANDS-OL。该方法依据帕累托法则,依托意见领袖评论大数据,采用优化的情感程度及初始改进率,结合KANO模型对用户关键需求进行挖掘。该方法不仅考虑了程度副词的语义信息,提高了情感分析的准确率,而且能够完成意见领袖的生成内容与KANO模型的自动整合,实现用户关键需求的获取与分类。研究结果表明,与挖掘所有评论的传统方法相比,KEY-DEMANDS-OL可以快速获取用户的关键需求,为企业制定产品优化方案提供辅助决策支持。

基于大数据技术的高校舆情分析模型研究

舆情分析系统的研发是辅助高校舆情治理的重要方式。针对现有系统在技术架构、数据采集和分析方面的不足,设计了基于流数据的舆情采集和存储技术框架,实现了基于LDA(Latent Dirichlet Allocation)的热点主题挖掘方法,提出了基于BERT(Bidirectional Encoder Representations from Transformers)预训练模型的情感分类方法,搭建了面向高校舆情分析的Web系统。为高校舆情分析系统的设计和实现提供有益的参考和解决方案。

“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。

基于SnowNLP的微博网络舆情分析系统

随着微博、抖音、贴吧等新兴网络社交媒体的发展,大量用户开始喜欢使用这些平台进行发布和获取信息,因此累积了大量舆情数据。为了能够及时监测网络舆论动向,更好地维护互联网的安全运营和网络安全,针对实时微博数据,研究设计了一种基于SnowNLP的微博网络舆情分析系统。该系统由舆情数据采集、舆情数据分析和舆情数据可视化组成,能够实现微博数据文本挖掘、网络舆情数据情感分析、舆情数据与关键词匹配结果统计等功能,并能够对微博内容情感分析结果、用户等级、内容分词结果等进行可视化展示。实验测试结果表明:该系统功能运行正常,同时验证了设计方案的可行性和有效性。系统在网络舆情监测领域具有重要的应用价值。

自动驾驶出行服务的公众关切与研究展望--兼评“萝卜快跑”世界最大规模无人驾驶商业化运营

基于社交媒体评论分析公众态度已经成为一种新兴的数据驱动研究方法,本文初步探索一种以舆情分析驱动科学研究的思路。2024年5月起,百度旗下的“萝卜快跑”自动驾驶出行服务平台在中国武汉开展世界上最大规模的全无人商业化运营,并因一起轻微交通事故在社交媒体上引发了舆情。首先,本文概述了“萝卜快跑”在武汉的商业化实践进程。随后,借助百度指数平台分析了公众对于“萝卜快跑”“无人驾驶”“无人驾驶出租车”关注度的演化。接着,通过对新浪微博和抖音社交媒体上的1809条热门评论开展词云分析和主题建模分析,挖掘出了公众关注的四个热点主题:技术进步、服务体验、市场竞争以及经济社会影响。最后,根据公众关切,从自动驾驶的社会兼容性提升、出行服务市场结构与政府规制、就业变革与社会保障制度、法律框架与地方立法探索四个方面提出了研究方向建议。本文结果有助于政府和学界及时了解中国公众对于自动驾驶出行服务的态度和关注点,主动谋划研究方向以响应社会需求,促进技术创新的同时主动防范风险,尽快塑造适应新质生产力发展的社会认知和外部环境,助力我国在全球自动驾驶出行服务领域的国际竞争中掌握主动权。