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Utilizing Social Media Data Analytics to Enhance Banking Services

Intelligent Information Management 2022年第1期14卷 1-14页

作者： Mohamed Abd El-Hamid Farag Ahmed Askar Amal Elsayed Aboutabl Amr Galal Business Information Systems Faculty of Commerce and Business Administration Helwan University Cairo Egypt Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Faculty of Commerce and Business Administration Helwan University Cairo Egypt

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.

关键词： Natural Language Processing (NLP) sentiment analysis Social Banking Information Retrieval (IR) Social Media Analytics

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Research on Prediction of sentiment Trend of Food Safety Public Opinion

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Journal of Computer and Communications 2023年第3期11卷 189-201页

作者： Chaofan Jiang Hu Wang Changbin Jiang Di Li Management School Wuhan University of Technology Wuhan China

Emotion has a nearly decisive role in behavior, which will directly affect netizens’ views on food safety public opinion events, thereby affecting the development direction of public opinion on the event, and it is of great significance for food safety network public opinion to predict emotional trends to do a good job in food safety network public opinion guidance. In this paper, the dynamic text representation method XLNet is used to generate word vectors with context-dependent dependencies to distribute the text information of food safety network public opinion. Then, the word vector is input into the CNN-BiLSTM network for local semantic feature and context semantic extraction. The attention mechanism is introduced to give different weights according to the importance of features, and the emotional tendency analysis is carried out. Based on sentiment analysis, sentiment value time series data is obtained, and a time series model is constructed to predict sentiment trends. The sentiment analysis model proposed in this paper can well classify the sentiment of food safety network public opinion, and the time series model has a good effect on the prediction of food safety network public opinion sentiment trend. .

关键词： Network Public Opinion sentiment analysis Time Series Prediction XLNet

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Emotion Deduction from Social Media Text Data Using Machine Learning Algorithm

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Journal of Computer and Communications 2023年第11期11卷 183-196页

作者： Thambusamy Velmurugan Baskaran Jayapradha PG and Research Department of Computer Science Dwaraka Doss Govardhan Doss Vaishnav College Chennai India PG and Research Department of Computer Science Dr. Ambedkar Government Arts College Chennai India

Emotion represents the feeling of an individual in a given situation. There are various ways to express the emotions of an individual. It can be categorized into verbal expressions, written expressions, facial expressions and gestures. Among these various ways of expressing the emotion, the written method is a challenging task to extract the emotions, as the data is in the form of textual dat. Finding the different kinds of emotions is also a tedious task as it requires a lot of pre preparations of the textual data taken for the research. This research work is carried out to analyse and extract the emotions hidden in text data. The text data taken for the analysis is from the social media dataset. Using the raw text data directly from the social media will not serve the purpose. Therefore, the text data has to be pre-processed and then utilised for further processing. Pre-processing makes the text data more efficient and would infer valuable insights of the emotions hidden in it. The preprocessing steps also help to manage the text data for identifying the emotions conveyed in the text. This work proposes to deduct the emotions taken from the social media text data by applying the machine learning algorithm. Finally, the usefulness of the emotions is suggested for various stake holders, to find the attitude of individuals at that moment, the data is produced. .

关键词： Data Pre-Processing Machine Learning Algorithms Emotion Deduction sentiment analysis

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Survey on Data analysis in Social Media:A Practical Application Aspect

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Big Data Mining and Analytics 2020年第4期3卷 259-279页

作者： Qixuan Hou Meng Han Zhipeng Cai the College of Computing Georgia Institute of TechnologyAtlantaGA 30332USA Data-driven Intelligence Research(DIR)Lab of College of Computing and Software Engineering Kennesaw State UniversityKennesawGA 30060USA the Department of Computer Science Georgia State UniversityAtlantaGA 30303USA

Social media has more than three billion users sharing events,comments,and feelings throughout the *** serves as a critical information source with large volumes,high velocity,and a wide variety of *** previous studies on information spreading,relationship analyzing,and individual modeling,etc.,have been heavily conducted to explore the tremendous social and commercial values of social media *** survey studies the previous literature and the existing applications from a practical *** outline a commonly used pipeline in building social media-based applications and focus on discussing available analysis techniques,such as topic analysis,time series analysis,sentiment analysis,and network *** that,we present the impacts of such applications in three different areas,including disaster management,healthcare,and ***,we list existing challenges and suggest promising future research directions in terms of data privacy,5 G wireless network,and multilingual support.

关键词： social media topic analysis time series analysis sentiment analysis network analysis disaster management bio-surveillance business intelligence

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The impact of Facebook on real estate sales

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Journal of Management Analytics 2021年第1期8卷 101-112页

作者： Hui Shi Zhongming Ma Dazhi Chong Wu He Computer Information Systems Department California State Polytechnic UniversityPomonaCAUSA School of Management California Lutheran UniversityThousand OaksCAUSA Information Technology&Decision Sciences Old Dominion UniversityNorfolkVAUSA

Businesses have been using social media to promote products and services to increase *** paper aims to study the impact of Facebook on real estate ***,we examine how realtors’activities on Facebook business pages are associated with real estate ***,we include time lags in analysis because a time lag can be expected between activates on Facebook and a resulting real estate *** the collected datasets,the results suggest that:(1)The total numbers of Facebook likes,links,and stories are positively associated with real estate sales;(2)The average sentiment score of Facebook posts is negatively associated with real estate sales;(3)The influence of activities on Facebook has a time lag effect on real estate *** research findings can be used by real estate stakeholders to promote and potentially forecast sales.

关键词： Facebook social media real estate sentiment analysis decision making

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Predicting motion picture box office performance using temporal tweet patterns

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International Journal of Intelligent Computing and Cybernetics 2018年第1期11卷 64-80页

作者： Najafi Hossein Darryl W.Miller Department of Computer Science and Information Systems University of Wisconsin–River FallsRiver FallsWisconsinUSA Department of Management and Marketing University of Wisconsin–River FallsRiver FallsWisconsinUSA

Purpose–The purpose of this paper is to investigate temporal tweet patterns and their effectiveness in predicting the financial performance of a ***,how tweet patterns are formed prior to and after a movie’s release and their usefulness in predicting a movie’s success is ***/methodology/approach–Volume was measured and sentiment analysis was performed on a sampleof Tweetsposted fourdays beforeand afterthe releaseof 86 *** patternof tweeting for financially successful movies was compared with those that were financial *** temporal tweet patterns,a number of machine learning models were developed and their predictive performance was ***–Results show that the temporal patterns of tweet volume,length and sentiment differ between“hits”and“busts”in the days surrounding their *** with“busts”the tweet pattern for“hits”reveal higher volume,shorter length,and more favourable *** patterns in social media features occur days in advance of a movie’s release and can be used to develop models for predicting a movie’s ***/value–analysis of temporal tweet patterns and their usefulness in predicting box office returns is the main contribution of this *** of this research could lead to development of analyticaltools allowingmotionpicture studiosto accurately predictand possiblyinfluencethe opening night box-office receipts prior to the release of the ***,the specific temporal tweet patterns presented by this work may be applied to problems in other areas of research.

关键词： Forecasting Social media Natural language processing sentiment analysis Machine learning Box office

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Natural language processing(NLP)in management research:A literature review

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Journal of Management Analytics 2020年第2期7卷 139-172页

作者： Yue Kang Zhao Cai Chee-Wee Tan Qian Huang Hefu Liu School of Management University of Science and Technology of ChinaHefeiPeople’s Republic of China Nottingham University Business School China University of Nottingham Ningbo ChinaNingboPeople’s Republic of China cDepartment of Digitalization Copenhagen Business SchoolCopenhagenDenmark

Natural language processing(NLP)is gaining momentum in management research for its ability to automatically analyze and comprehend human ***,despite its extensive application in management research,there is neither a comprehensive review of extant literature on such applications,nor is there a detailed walkthrough on how it can be employed as an analytical *** this end,we review articles in the UT Dallas List of 24 Leading Business Journals that employ NLP as their focal analytical technique to elucidate how textual data can be harnessed for advancing management theories across multiple *** describe the available toolkits and procedural steps for employing NLP as an analytical technique as well as its advantages and *** so doing,we highlight the managerial and technological challenges associated with the application of NLP in management research in order to guide future inquires.

关键词： natural language processing literature review sentiment analysis machine learning

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Token-wise sentiment decomposition for ConvNet:Visualizing a sentiment classifier

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Visual Informatics 2020年第2期4卷 132-141页

作者： Piyush Chawla Subhashis Hazarika Han-Wei Shen The Ohio State University Columbus43210USA

Convolutional neural networks are one of the most important and widely used constructs in natural language processing and AI in *** many applications,they have achieved state-of-the-art performance,with training time faster than the other ***,due to their limited interpretability,they are less favored by practitioners over attention-based models,like RNNs and self-attention(Transformers),which can be visualized and interpreted more intuitively by analyzing the attention-weight *** this work,we present a visualization technique that can be used to understand the inner workings of text-based CNN *** also show how this method can be used to generate adversarial examples and learn the shortcomings of the training data.

关键词： Convolutional neural networks Visualization sentiment analysis

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Phrase-Level sentiment Polarity Classification Using Rule-Based Typed Dependencies and Additional Complex Phrases Consideration

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Journal of Computer Science & Technology 2012年第3期27卷 650-666页

作者：陈坚永罗镇川邓燕玲张圭煜 Wee Kim Wee School of Communication and Information Nanyang Technological University31 Nanyang Link 637718Singapore Wee Kim Wee School of Communication and Information Nanyang Technological University31 Nanyang Link 637718SingaporeMemberACM School of Computer Engineering Nanyang Technological UniversityBlock N4 Nanyang Avenue639798Singapore

The advent of Web 2.0 has led to an increase in user-generated content on the Web. This has provided an extensive collection of free-style texts with opinion expressions that could influence the decisions and actions of their readers. Providers of such content exert a certain level of influence on the receivers and this is evident from blog sites having effect on their readers＇ purchase decisions, political view points, financial planning, and others. By detecting the opinion expressed, we can identify the sentiments on the topics discussed and the influence exerted on the readers. In this paper, we introduce an automatic approach in deriving polarity pattern rules to detect sentiment polarity at the phrase level, and in addition consider the effects of the more complex relationships found between words in sentiment polarity classification. Recent sentiment analysis research has focused on the functional relations of words using typed dependency parsing, providing a refined analysis on the grammar and semantics of textual data. Heuristics are typically used to determine the typed dependency polarity patterns, which may not comprehensively identify all possible rules. We study the use of class sequential rules （CSRs） to automatically learn the typed dependency patterns, and benchmark the performance of CSR against a heuristic method. Preliminary results show CSR leads to further improvements in classification performance achieving over 80% F1 scores in the test eases. In addition, we observe more complex relationships between words that could influence phrase sentiment polarity, and further discuss on possible approaches to handle the effects of these complex relationships.

关键词： class sequential rule complex phrase sentiment analysis typed dependency

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Public perception of electric vehicles on reddit over the past decade

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Communications in Transportation Research 2022年第1期2卷 181-192页

作者： Tao Ruan Qin Lv Department of Computer Science University of Colorado Boulder430 UCBBoulderCO80309USA

Understanding public perception of electric vehicles(EVs)is imperative for increasing EV adoption,which can significantly reduce greenhouse gas(GHG)emissions,thereby mitigating climate change and global *** most existing research characterizes public perception of EV in the context of surveys,questionnaires,or interviews,our study leverages Reddit online social network(OSN)data to capture EV public perception at a much larger *** have collected 3,437,917 Reddit posts(including 274,979 submissions and 3,162,938 comments)between January 2011 and December 2020 relevant to EVs and analyzed them along several axes to understand how EVs are perceived by the public on Reddit through the following research questions:(1)What EVrelated topics have been discussed by Reddit users?Whether/how Reddit users’interest in different topics has changed during 2011–2020?(2)What sentiment do Reddit users hold towards EVs?Whether public sentiment on Reddit has shifted over the past 10 years?(3)Whether/how do various Reddit communities(i.e.,subreddits)have different perceptions of EVs?Our analysis evinces the potential of utilizing a large-scale OSN dataset for demonstrating a much wider spectrum of topics that the public is interested in than previous studies show,reveals fringe communities including r/conspiracy have many(controversial)discussions on EVs,especially on the environmental impacts of EVs,and one political community(r/The_Donald)has similar patterns with fringe communities in both sentiment and topic *** answering these research questions,we aim to develop a more comprehensive understanding of the public perception of EVs in the past decade.

关键词： Reddit Electric vehicles Public perception Online social network Topic modeling sentiment analysis

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