Twitter Sentiment Analysis of the Accounting Profession in Social Media

Nowadays,the impact of emerging social media on the accounting is still a relatively new field and none of the existing research has explored the correlation among the public attitude towards social media,official accounting attitude and the performance of the stock prices of listed firms.U sing the state-of-the-art sentiment analysis tool and 25 public companies'dataset from Yahoo Finance,the correlations among the company's stock price,sentiment in twitter and sentiment in earnings report are quantitatively studied in this paper.Hypothesis testing is used to infer the result of two proposed hypotheses on the sample data.The results demonstrate that(1)there is a significant negative correlation between company's stock price and sentiment in its corresponding earnings reports,and(2)there is no statistical significance for the correlation between company's stock price and sentiment in its corresponding Twitter data.

Sentiment Analysis on the Social Networks Using Stream Algorithms

The rising popularity of online social networks (OSNs), such as Twitter, Facebook, MySpace, and LinkedIn, in recent years has sparked great interest in sentiment analysis on their data. While many methods exist for identifying sentiment in OSNs such as communication pattern mining and classification based on emoticon and parts of speech, the majority of them utilize a suboptimal batch mode learning approach when analyzing a large amount of real time data. As an alternative we present a stream algorithm using Modified Balanced Winnow for sentiment analysis on OSNs. Tested on three real-world network datasets, the performance of our sentiment predictions is close to that of batch learning with the ability to detect important features dynamically for sentiment analysis in data streams. These top features reveal key words important to the analysis of sentiment.

User Profile & Attitude Analysis Based on Unstructured Social Media and Online Activity

As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.

Enhancing Sentiment Analysis on Twitter Using Community Detection

The increasing popularity of social media in recent years has created new opportunities to study the interactions of different groups of people. Never before have so many data about such a large number of individuals been readily available for analysis. Two popular topics in the study of social networks are community detection and sentiment analysis. Community detection seeks to find groups of associated individuals within networks, and sentiment analysis attempts to determine how individuals are feeling. While these are generally treated as separate issues, this study takes an integrative approach and uses community detection output to enable community-level sentiment analysis. Community detection is performed using the Walktrap algorithm on a network of Twitter users associated with Microsoft Corporation’s @technet account. This Twitter account is one of several used by Microsoft Corporation primarily for communicating with information technology professionals. Once community detection is finished, sentiment in the tweets produced by each of the communities detected in this network is analyzed based on word sentiment scores from the well-known SentiWordNet lexicon. The combination of sentiment analysis with community detection permits multilevel exploration of sentiment information within the @technet network, and demonstrates the power of combining these two techniques.

Mutually Enhancing Community Detection and Sentiment Analysis on Twitter Networks

The burgeoning use of Web 2.0-powered social media in recent years has inspired numerous studies on the content and composition of online social networks (OSNs). Many methods of harvesting useful information from social networks’ immense amounts of user-generated data have been successfully applied to such real-world topics as politics and marketing, to name just a few. This study presents a novel twist on two popular techniques for studying OSNs: community detection and sentiment analysis. Using sentiment classification to enhance community detection and community partitions to permit more in-depth analysis of sentiment data, these two techniques are brought together to analyze four networks from the Twitter OSN. The Twitter networks used for this study are extracted from four accounts related to Microsoft Corporation, and together encompass more than 60,000 users and 2 million tweets collected over a period of 32 days. By combining community detection and sentiment analysis, modularity values were increased for the community partitions detected in three of the four networks studied. Furthermore, data collected during the community detection process enabled more granular, community-level sentiment analysis on a specific topic referenced by users in the dataset.

Investigating User Ridership Sentiments for Bike Sharing Programs

Bike sharing is considered a state-of-the-art transportation program. It is ideal for short or medium trips providing riders the ability to pick up a bike at any self-serve bike station and return it to any bike station located within the system’s coverage area. The bike sharing programs in the United States are still very young compared to those in European countries. Washington DC was the first jurisdiction to devise a third generation bike sharing system in the US in 2008. To evaluate the popularity of a bike sharing program, a sentiment analysis of the riders’ feedback can be performed. Twitter is a great platform to understand people’s views instantly. Social media mining is, thus, gaining popularity in many research areas including transportation. Social media mining has two major advantages over conventional attitudinal survey methods—it can easily reach a large audience and it can reflect the true behavior of participants because of the anonymity social media provides. It is known that self-imposed censor is common in responding to conversational attitudinal surveys. This study performed text mining on the tweets related to a case study (Capital Bike share of Washington DC) to perform sentiment analysis or opinion mining. The results of the text mining mostly revealed higher positive sentiments towards the current system.

Predicting the Stock Price Movement by Social Media Analysis

Prediction of stock trend has been an intriguing topic and is extensively studied by researchers from diversified fields. Machine learning, a well-established algorithm, has been also studied for its potentials in prediction of financial markets. In this paper, seven different techniques of data mining are applied to predict stock price movement of Shanghai Composite Index. The approaches include Support vector machine, Logistic regression, Naive Bayesian, K-nearest neighbor classification, Decision tree, Random forest and Adaboost. Extracting the corresponding comments between April 2017 and May 2018, it shows that: 1) sentiment derived from Eastmoney, a social media platform for the financial community in China, further enhances model performances, 2) for positive and negative sentiments classifications, all classifiers reach at least 75% accuracy and the linear SVC models prove to perform best, 3) according to the strong correlation between the price fluctuation and the bullish index, the approximate overall trend of the closing price can be acquired.

Utilizing Social Media Data Analytics to Enhance Banking Services

The two most important challenges facing banks today are attracting new customers and retaining their existing ones. Research shows that 30 percent of banks cited customer loyalty as their biggest challenges. Thus, given that customer loyalty is completely connected to customer delight. The challenging question is: How do banks achieve customer delight by making every interaction a pleasant experience? In our viewpoint “The key is to stop treating customers as segments and personalize all customer interactions and services which can be achieved by using the latest technological advancements in Big Data Analytics, Artificial Intelligence (AI) and Machine Learning”. With the rapidly increasing usage of social media like Facebook, Twitter, LinkedIn, and Instagram, business organizations are now moving towards adapting this technology to drive business advantages. This research will explore the power of social media and how it can be used by banks to provide an edge over their competitors by providing improved products and services to their customers thereby making their experience easy and responsive. It also proposes a framework for social media analytics and its important components to address all the technical and business aspects of the retail and online banking, however, what customer expects from this medium and what banks offer to them needs to be widely studied and understood.

乡村文化传承与空间重构的逻辑辨析和重构路径

基于场域理论,从乡村文化场域的物质要素和非物质要素两方面研究建构乡村文化传承与空间重构的逻辑框架,探索在乡镇级国土空间规划中乡村文化场域的空间重构路径。以北京妙峰山镇为例,结合实际调查数据与社交媒体数据,从场域—资本—惯习的动态变化及三者的匹配关系出发,剖析妙峰山镇现存的社会、制度、经济与生态问题及其原因,从文化认同、文化创新、文化传承的视角提出乡村文化场域空间重构的路径,通过重构健康的制度场域和永续的社会场域、高效的经济场域、和谐的生态场域,探索新场所与乡土文化融合模式,推进乡村振兴及韧性发展。

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。

Big Data Study for Gluten-Free Foods in India and USA Using Online Reviews and Social Media

Celiac disease, gluten-allergy or gluten-sensitivity is caused due to glutamine protein from the grains like wheat, rye and barley (collectively called gluten). This protein damages the small intestine and causes stomach pain, bloating, weakness etc. Celiac disease, gluten-allergy or gluten-sensitivity has never really been taken seriously in developing countries like India. However, in developed nations like UK, USA, Canada and other parts of Europe, gluten-free foods have become quite popular. With a prevalence rate of about one in 100 - 133 people worldwide, celiac disease is widespread across the globe and life-long consumption of gluten-free food is recommended treatment for this allergy. Apart from celiac-disease patients, gluten-free foods are also consumed by health conscious people for weight management and high protein diet and by the patients for diabetes, autism and food allergies. Apart from gluten-free flour, biscuits, cookies and snacks, product innovations like gluten-free beers are becoming very popular. Big data including online blogs, articles, and reviews have played a major role in increased sales of gluten-free foods. Thus, analysis of editorial and social media content becomes essential to understand the leading trends in gluten-free foods. This study provided deep insights about positive, negative and neutral sentiments related to gluten-free foods using the data from Perspectory Media Insights and Google Trends. This study also revealed that most of the consumers talked and expected product innovation in food sections like snacks, fast food (pizza, pasta and noodles) and desserts through comments on social and editorial media. Searches were divided into developed (e.g., U.S.A.) and developing nations (e.g., India) to get more details about the consumer preferences. This study would help manufacturers of gluten-free foods to develop food products according to the choices and preferences of consumers. The study is very unique in itself since it combines big data to niche food market of gluten-free foods to draw the valuable consumer preferences using online platforms.

基于社交媒体数据的城市洪涝灾害信息智能提取与分析

近年来,由于气候变化导致极端降雨引起的城市内涝灾害事件频发,给我国城市水安全和可持续发展带来威胁,准确掌握受灾区域的舆论主体和公众情绪,对提高应急管理部门内涝灾害的态势感知能力具有重要意义。在当今智能网络时代,人们通过社交媒体反映问题和建议的诉求日益凸显,社交媒体已逐渐成为反映民众情感和社会舆情的主要载体,为获取自然灾害信息提供了新的途径。如何从社交媒体中快速提取城市洪涝灾害信息,并对自然灾害信息进行主题分类和情感分析,准确掌握区域灾情的主题类别和民众舆论倾向,是目前亟待解决的关键技术问题。以新浪微博为例,阐述了洪涝灾害数据的获取与预处理方法,构建了基于FastText的城市洪涝灾害信息主题分类和情感分析模型,以准确掌握受灾区域的主题类别和舆论导向。以2021年郑州“7.20”特大暴雨期间洪涝灾害为例的研究结果表明,本文方法实现了对社交媒体中城市洪涝灾害数据的智能提取与分析,主题分类模型对预设八种类别数据的分类预测F1值达到0.80以上,且情感分析模型基本能够准确预测情感标记为“负面”的数据,这表明本文构建的基于FastText的城市洪涝灾害信息主题分类和情感分析模型能够满足支撑城市应急管理部门动态掌握洪涝灾害发展态势及公众情绪的需求,对防涝减灾调度、安抚民众情绪和实时定点救援等工作具有重要的指导意义。

情感分歧对社交媒体中信息再传播的影响——以微博为例

[研究目的]社交媒体中信息的再传播是目前主流传播方式之一,研究情感特征对社交媒体中信息再传播的作用,揭示情感信息影响社交媒体中信息再传播的机制,对于科学引导网络舆论、维持健康网络环境具有重要意义。[研究方法]基于认知失调理论,引入情感分歧特征进行情感分析,以社交媒体中帖子的转发数与评论数作为信息再传播效果的衡量指标,构建了情感分歧与情感倾向影响信息再传播的理论模型,并使用微博平台上的数据,对模型进行验证。[研究结论]通过对回归模型结果分析,情感分歧能够显著正向影响社交媒体信息再传播,促进帖子的转发与评论;正面情感倾向性能够显著促进帖子的转发效果,对帖子的评论数影响效果并不显著;帖子内容的情感倾向性能够调节帖子内容中的情感分歧对帖子转发的影响,而评论情感倾向能够调节评论内容中的情感分歧对帖子评论数的影响。

人本、情本、民本:苏东坡的家国情怀

苏轼不仅是著名的文学家,也是伟大的思想家,更是一个活生生的仁人志士,具有鲜活的家国情怀和高尚志趣。他把个人价值寄托在对国家和人民的大爱与奋斗中,以伟岸的人格承接使命担当,以家国情怀托举利民使命,以短暂一生铸就千古英名。从宇宙观考虑,苏轼继承了夏禹以来“敬鬼敬神而远之”的传统,在天地人神之间,他主“人本”说;他承认人生而有欲,感物有情,教化的功能端在于对人情的节制,因此在人性论方面,他主“情本”说;他认为“民可亲而不可疏,可敬而不可愚”,于君臣社稷之间,他主“民本”说。通过这些方面,可以完整地透视出苏轼的宇宙观、社会观和人生观。

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。

“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。

社交媒体数据支持的城市承灾体脆弱性评估——以深圳极端天气为例

世界范围内极端天气频度和强度的显著增强,对高密度城市中公众和基础设施等承灾体造成严峻威胁,揭示和评估城市承灾体脆弱性对提升城市韧性具有重大意义。相对传统数据在极端突发灾害事件中的应用局限,社交媒体数据包含了实时且丰富时空信息而呈现出巨大潜力。研究以2018年“山竹”台风灾害3个时间序列阶段和深圳76个街道的空间分布为例,获取并预处理灾害期间相关微博数据后,分别基于LDA模型和SnowNLP模型训练得到公众灾害感知脆弱性和公众情绪脆弱性,并基于语料库构建进一步细分得到6种类型的基础设施脆弱性。研究发现这3种脆弱性的集中峰值呈现依次滞后的时间序列特征以及南北圈层差异和“哑铃状”的空间分布特征。最后针对性提出提升城市承灾体脆弱性时空协调性的相关建议,对城市灾害韧性提供一定规划指引。

气候变化背景下融合社交媒体情感与多源数据的洪涝损失估算

提取2021年7月10日—2023年4月10日新浪微博的洪涝灾害文本,基于朴素贝叶斯算法实现暴雨洪涝情感分析,构建了一种融合核心致灾因子、承灾载体和社交媒体实时数据的洪涝灾损估算(ISFRD)模型。结果表明:在社交媒体中,暴雨洪涝灾害的情感峰值主要集中在6—8月,峰值变化和洪涝灾害热点事件讨论具有强同步关系;洪灾期间情感波动变化,洪涝损失与平均情感值具有反向关系;ISFRD洪涝灾损模型可以有效评估省(市)级尺度、不同受灾程度的暴雨洪涝事件,估算结果精度较高(平均准确率>90%,MAE=27.04,RMSE=45.26)。在日益复杂的气候环境下,该模型可为洪涝灾损快速厘定、防灾减灾和舆论引导提供一定参考。

综合交通枢纽内部公共空间公众情绪感知的时空差异与影响因素

在信息化时代,如何借助公众情绪感知来优化公共空间品质,成为提升公众既有空间体验的思考之一。结合环境心理学,以上海虹桥综合交通枢纽为例,利用新浪微博数据与自然语言处理技术来分析综合交通枢纽内部公共空间的公众情绪感知,并通过情绪词频分析法探究公众情绪的影响因素。研究发现:第一,综合交通枢纽内部公共空间的公众情绪感知存在显著的时空差异;第二,时空要素与主观要素均会导致公众在综合交通枢纽内部公共空间不同情绪的感知差异;第三,影响公众情绪感知的因素存在“认知”与“行为”维度的不足。在此基础上,验证了通过社交媒体数据来揭示综合交通枢纽内部公共空间的公众情绪感知的可行性,提出符合公众中高阶情感需求的空间改善与提升策略,以期为综合交通枢纽公共空间营造提供借鉴。