Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

Enhancing User Security on Instagram: A Multifaceted AI System for Filtering Abusive Comments

Social media platforms like Instagram have increasingly become venues for online abuse and offensive comments. This study aimed to enhance user security to create a safe online environment by eliminating hate speech and abusive language. The proposed system employed a multifaceted approach to comment filtering, incorporating the multi-level filter theory. This involved developing a comprehensive list of words representing various types of offensive language, from slang to explicit abuse. Machine learning models were trained to identify abusive messages through sentiment analysis and contextual understanding. The system categorized comments as positive, negative, or abusive using sentiment analysis algorithms. Employing AI technology, it created a dynamic filtering mechanism that adapted to evolving online language and abusive behavior. Integrated with Instagram while adhering to ethical data collection principles, the platform sought to promote a clean and positive user experience, encouraging users to focus on non-abusive communication. Our machine-learned models, trained on a cleaned Arabic language dataset, demonstrated promising accuracy (75.8%) in classifying Arabic comments, potentially reducing abusive content significantly. This advancement aimed to provide users with a clean and positive online experience.

Additional comments on foot reflexology treatment for sensorineural hearing loss in infant

Currently,treatment options for infant sensorineural hearing loss(SNHL)are limited.This article describes a novel case of SNHL in an infant successfully treated with foot reflexology,along with observed brain activity changes before and after treatment,as indicated by functional magnetic resonance imaging.Hence,this commentary discusses the case and our viewpoints regarding foot reflexology for treating SNHL.

Emotional differences based on comments on doctor-patient disputes with varying levels of severity

BACKGROUND The risks associated with negative doctor-patient relationships have seriously hindered the healthy development of medical and healthcare and aroused wide-spread concern in society.The number of public comments on doctor-patient relationship risk events reflects the degree to which the public pays attention to such events.Thirty incidents of doctor-patient disputes were collected from Weibo and TikTok,and 3655 related comments were extracted.The number of comment sentiment words was extracted,and the comment sentiment value was calculated.The Kruskal-Wallis H test was used to compare differences between each variable group at different levels of incidence.Spearman’s correlation analysis was used to examine associations between variables.Regression analysis was used to explore factors influencing scores of comments on incidents.RESULTS The study results showed that public comments on media reports of doctor-patient disputes at all levels are mainly dominated by“good”and“disgust”emotional states.There was a significant difference in the comment scores and the number of partial emotion words between comments on varying levels of severity of doctor-patient disputes.The comment score was positively correlated with the number of emotion words related to positive,good,and happy)and negatively correlated with the number of emotion words related to negative,anger,disgust,fear,and sadness.CONCLUSION The number of emotion words related to negative,anger,disgust,fear,and sadness directly influences comment scores,and the severity of the incident level indirectly influences comment scores.

On the Linguistic Features of Comments in Micro-blogs from Different Social Status

The study makes a contrastive analysis of the differences in the comments of people from different social status in their Mini-blogs.It aims to find the manifestations of those differences by analyzing the underlying causes and provide constructive suggestions on how to build a more harmonious public platform,which is of its great significance.

Syntax-Based Aspect Sentiment Quad Prediction by Dual Modules Neural Network for Chinese Comments

Aspect-Based Sentiment Analysis(ABSA)is one of the essential research in the field of Natural Language Processing(NLP),of which Aspect Sentiment Quad Prediction(ASQP)is a novel and complete subtask.ASQP aims to accurately recognize the sentiment quad in the target sentence,which includes the aspect term,the aspect category,the corresponding opinion term,and the sentiment polarity of opinion.Nevertheless,existing approaches lack knowledge of the sentence’s syntax,so despite recent innovations in ASQP,it is poor for complex cyber comment processing.Also,most research has focused on processing English text,and ASQP for Chinese text is almost non-existent.Chinese usage is more casual than English,and individual characters contain more information.We propose a novel syntactically enhanced neural network framework inspired by syntax knowledge enhancement strategies in other NLP studies.In this framework,part of speech(POS)and dependency trees are input to the model as auxiliary information to strengthen its cognition of Chinese text structure.Besides,we design a relation extraction module,which provides a bridge for the overall extraction of the framework.A comparison of the designed experiments reveals that our proposed strategy outperforms the previous studies on the key metric F1.Further experiments demonstrate that the auxiliary information added to the framework improves the final performance in different ways.

Tackling Faceless Killers: Toxic Comment Detection to Maintain a Healthy Internet Environment

According to BBC News,online hate speech increased by 20%during the COVID-19 pandemic.Hate speech from anonymous users can result in psychological harm,including depression and trauma,and can even lead to suicide.Malicious online comments are increasingly becoming a social and cultural problem.It is therefore critical to detect such comments at the national level and detect malicious users at the corporate level.To achieve a healthy and safe Internet environment,studies should focus on institutional and technical topics.The detection of toxic comments can create a safe online environment.In this study,to detect malicious comments,we used approxi-mately 9,400 examples of hate speech from a Korean corpus of entertainment news comments.We developed toxic comment classification models using supervised learning algorithms,including decision trees,random forest,a support vector machine,and K-nearest neighbors.The proposed model uses random forests to classify toxic words,achieving an F1-score of 0.94.We analyzed the trained model using the permutation feature importance,which is an explanatory machine learning method.Our experimental results confirmed that the toxic comment classifier properly classified hate words used in Korea.Using this research methodology,the proposed method can create a healthy Internet environment by detecting malicious comments written in Korean.

Critical Discourse Analysis Based on Halliday’s Systemic Functional Linguistics: Taking The Economist’s First Commentation on the End of China’s Zero-COVID Policy as an Example

Based on Halliday’s systemic functional grammar,especially the ideational function,this research aims at disclosing the hidden ideologies and values of the seemingly objective news reports on China’s COVID-19 policies in The Economist.Transitivity,voice,and nominalization are the major analytical subjects.After China lifted the zero-COVID policy,western media began criticizing China’s lack of data sharing,with some misinformation and misleading reports.The denouncement of inertness and reluctance to fight against the pandemic disclaim the Chinese government’s efforts and depreciate China’s image.China is portrayed as the villain and destroyer of people’s health worldwide.Meanwhile,they also hold a hesitant attitude toward China’s diplomacy.The re-engaging with foreign countries and travel restrictions have been described as imprudent and rushed actions.They also consider China as the fuse of contradiction in the United Nations.What is overt is their view of breaking up China.

生成式人工智能支持的教师评语研究:基于初中数学课堂的实践探索

教师评语是形成性评价分析的重要数据来源,但现有教师评语生成的质量在社会性、客观性和个性化方面存在一定的不足,生成式人工智能支持的教师评语为解决该问题提供了可能。生成式教师评语是指教师通过认知诊断技术得到的智能诊断数据与生成式人工智能平台进行交互,从而让生成式人工智能平台模拟教师社会性语言,生成数据驱动的个性化教师评语。在初中数学课堂中的实证研究发现:生成式教师评语有效地提高了学生的自我调节学习能力(Cohen's d=1.08,p=0.000)和学习动机(Cohen's d=0.59,p=0.003),对学生的深度访谈结果表明,生成式评语能作为考试的一种总结反思工具,体现了教师对学生个性化的关注和鼓励。研究结果为人工智能技术助力个性化、精准化教学提供了研究思路,为教育环境中持续评估和改进生成式人工智能技术的使用提供了建议。

IP属地化政策对舆情评论的影响研究——基于微博用户属地公开的准自然实验

[目的/意义]实证评估公开属地政策能否以及如何对舆情评论产生影响,为网络舆情研究、平台优化和治理提供启示。[研究设计/方法]基于自我监控理论和隐私计算理论构建研究模型和假设,以微博“IP属地”功能的推出为准自然实验,运用双重差分法和断点回归模型探究IP属地化政策对舆情评论的影响。[结论/发现]政策引发用户自我监控效应和风险感知效应,不仅整体改善了评论情感倾向、评论可读性和共情性,也能够显著抑制评论情感极性,该结论在一系列稳健性检验后仍然成立。政策影响在不同用户群体之间存在显著差异,相较处于事件热点中心的用户,属地公开更能促使处于非热点中心的用户做出改变。政策同时加剧评论中的地域歧视现象。[创新/价值]揭示了IP属地化政策对在线评论行为的作用,为清朗网络环境提供新视角,丰富了IP属地化与舆情评论行为影响因素的相关研究。

基于评论挖掘消费者偏好的蓝牙耳机设计策略

目的 本研究旨在从在线评论数据中挖掘消费者偏好信息,以探索蓝牙耳机设计的改良策略。方法 使用Python爬虫抓取目标消费者评论,并通过数据挖掘和文本分析定位需求主题和关键词后,基于消费者偏好理论,对产品功能属性进行情感分析和需求描述以控制变量,运用Mixed-logit模型提取消费者偏好特征以寻求最优的设计策略。结果 通过定量的实证研究,回归分析了偏好特征参数的显著性表达,生成蓝牙耳机改良策略实例并通过满意度检测验证其可行性。结论 结合评论挖掘和Mixed-logit模型,不仅能够设计出以消费者偏好为核心的产品设计方案,还可以为相关产品的设计策略改进提供指导与建议,具有一定的创新性和实用性。

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.

引用评论证据视角下高水平论文遴选研究

论文作为科研成果的重要表现形式,在“破五唯”的大背景下,科学遴选高水平论文至关重要,是保障代表作评价工作落地的基本抓手。引用评论是学术共同体对论文价值最直接的认可,亦是学术评价的实质性关键证据,对其内容进行深入挖掘有助于更科学地发现高水平论文。首先,本文基于学术认同理论和证据特征阐释了模型构建的基本思想;其次,对基于内容语义的引用情感和引用功能进行重新界定和分类,并充分考虑“施引作者可信度”,构建高水平论文遴选综合加权模型;再其次,选取化学领域顶级刊物Angewandte Chemie-International Edition中的相关论文展开实证研究,结果表明,本文提出的模型可以较精准地筛选出“非常重要论文(very important paper,VIP)”;最后,与其他主流评价指标进行比较分析,证实了本文模型具有较高的区分度和鉴别度,在论文学术水平评价中具有显著优越性。

媒介文化的折射:在线读书会读者的评论特征研究

数字化阅读和社交化分享催生出网络读书会的新景观。弹幕评论形式的出现,使在线读书会生成了基于播放时间和读者评论时间两种不同时间线的读者评论文本。文章对比分析传统评论和弹幕评论在行为和内容两方面的不同特征,认为不同的评论形式会显著影响读者的评论内容。具体而言,弹幕更倾向于社交娱乐的浅层交流,从时间维度延长读书会的讨论热度;而传统评论则更聚焦书籍内容的讨论,在空间维度呈现深度讨论特征。通过回溯阅读交流的历史场景,发现评论媒介偏向的背后折射出以原生口语文化、书面文化和次生口语文化为表征的媒介文化已在环境和形式两个方面,以其内化的主导性传播模式塑造了在线读书会读者的思维方式和交流模式,从而为数字时代的读者评论行为特征提供了理论注解。

从网络评价语料库看中国文学人物形象的重塑效果——以《射雕英雄传》中郭靖人物形象英译为例

金庸的长篇武侠小说《射雕英雄传》塑造了一系列个性化的人物形象,具有“英雄史诗”风格。以“豆瓣读书”和“好读网”上中英文读者对《射雕英雄传》的网络评价语料为基础,自建原文读者和译文读者评价语料库,通过比较中英读者对《射雕英雄传》中郭靖人物形象特征评价的异同,会发现英文读者对郭靖的某些性格特征的评价趋于扁平化,反映了郭靖人物形象在英译本中的重塑效果。英文读者对人物形象中蕴含中国特色的异质文化要素缺乏足够的知识储备,因此,在英译过程中译者应采取语境信息充实的翻译策略,适当为英文读者补充所缺失的语境信息,让读者以此为线索,推理出异质文化所传递的隐含意义,从而使读者对郭靖人物形象的解读更为丰满,以进一步提升翻译质量。

北京市某三级口腔专科医院门急诊处方不合理情况分析

目的 点评分析北京大学口腔医学院口腔医院门急诊处方,了解处方质量和临床用药存在的问题,为促进合理用药提供参考。方法 随机抽取2021年7—12月该院门急诊处方6 000张,利用美康合理用药软件进行用药合理性初步筛查,再由药师进行人工复审,采用Excel软件对不合理处方进行分类统计及分析。结果 6 000张处方中,软件初筛不合理处方798张(含药品不合理条目828条),不合理率为13.30%。药师人工复审后确认不合理处方214张(含药品不合理条目219条),不合理率为3.57%,不合理条目类型为用法用量不适宜(112条,51.14%)、诊断描述不全(59条,26.94%)、遴选药品不适宜(15条,6.85%)、书写不规范(14条,6.39%)。不合理条目分布最多的科室为口腔黏膜科,占比50.68%。不合理条目数最多的药品为他克莫司软膏,占比30.59%,主要问题为用药频次不合理(超过规定次数)。结论 医院门急诊处方基本合理,但需关注用法用量、诊断描述等问题。合理用药软件有助于处方点评初筛但无效提醒较多、需定期改进,点评结果需要药师结合医药进展及临床实际情况进行人工审核后最终判定。可通过软件初筛结合人工复审并及时反馈问题、加强超说明书用药管理等手段,加强临床合理用药。

全覆盖处方点评对门、急诊抗菌药物合理应用的影响

目的观察对门、急诊所有抗菌药物处方进行全覆盖大样本量的点评对医院抗菌药物合理用药影响。方法对门、急诊所有抗菌药物处方进行点评,本文通过实践前后对照的方法,将点评模式优化前(2022年)、优化后(2023年)的点评结果即不合格率进行比较和统计学处理,计数资料采用χ2检验。结果对比优化前,门急诊抗菌药物处方不合格率进一步降低。2023年与2022年相比明显降低,差异有统计学意义(P<0.05)。结论通过全覆盖处方点评、人工点评结合软件点评、学术交流和行政干预并行等措施可显著降低抗菌药物处方不合格率,持续改进不合理用药现象。

基于财务报告概念框架的我国自然资源资产负债表研究述评

从中共十八届三中全会提出探索编制自然资源资产负债表以来,理论与实务界对此进行了大量的理论研究与编报实务探索。以财务报告概念框架为基本视角,以样本文献为基础,揭示了自然资源资产负债表的研究领域和内容的变化情况,对自然资源资产负债表的概念、报告主体、报表要素确认与计量及报表格式等研究情况进行全面梳理。研究表明,现有文献在自然资源资产负债表的定位、报告主体、报表要素确认条件与计量方法、信息披露等方面存在一定不足,不同学者的研究基础与研究路径差异较大且离形成共识尚存在差距。基于现有研究情况和自然资源国家治理的现实需要,提出在明确界定报告主体和目标的前提下,应从统计学和会计学两个路径展开独立研究,对不同路径下的报表要素确认、计量与披露开展深入探讨。

虚假评论网络的识别与特征发现

为从网络结构特征的角度批量识别日益专业化的虚假购物评论,通过网络行为综合可疑得分和基于组筛的聚类分析快速找到相似的评论者网络,再从消费微观决策的视角比较4类产品真实评论与虚假评论的特征差异,提炼虚假评论的识别规则.结果表明,虚假评论网络存在典型的群组结构特征,与真实评论不同,虚假评论以目标产品为媒介有着非常紧密的联系;在“评论者–产品–评论”所形成的网络拓扑结构中,产品类型是虚假评论网络结构中的核心,决定了虚假评论群组的攻击模式.虚假评论最终表现出来的异质性也与产品类型有关,这意味着根据不同的产品类型设计差异化的虚假评论识别策略可以提高准确率.研究结果为规制虚假评论对网购的负面影响提供了一定参考.

用户弹幕评论行为影响因素实证分析

文章在社会认知理论的基础上对用户弹幕评论相关文献进行梳理,结合专家咨询法确定出4方面15项影响因素,采用解释结构模型(ISM)和交叉影响矩阵相乘法(MICMAC)分析用户弹幕评论行为影响因素之间的关联路径和层级关系,并对其结果进行讨论。研究发现:表达需求、信息需求、视频热度等因素对用户参与弹幕评论行为影响较大;内容价值性、网站信任等因素起到承上启下的作用;感知认同、感知互动等因素驱动力低、依赖性高,易受其他因素影响;可以通过提升用户信息素养、提高视频内容质量和优化用户参与体验等措施引导用户弹幕评论行为。