A Data Mining for the Effect of Cognitive Style, Subjective Emotion, and Physiological Phenomena on the Accuracy of Judgmental Time-Series Forecasting

Data mining is finding hidden rules in given dataset using non-traditional methods. The objective is to discover some useful tendency or patterns from the given collection of data. We had mined the rules representing the effect of cognitive style, subjective emotion, and physiological phenomena on the accuracy of subjects\u27 judgmental time-series forecasting. Then we have tried to find out any consistent tendencies in the frequent rules. Subjects in Analytic-style show more accurate forecasting. Subjects in relaxed mode show more accurate forecasting. And Subjects’ left EEG and beta rhythm seem to have a significant effect on their forecasting accuracy. But additional data mining to the other effects should be made

The Business Impact of Social Media - Sentiment Analysis Approach -

이 연구의 목적은 소셜 미디어에서 추출된 7개의 감성 도메인이 자동차 시장 점유율 예측에 대한 감성 분석 실험을 위한 데이터로서 적합한 지에 대한 신뢰성을 확인하고 고객들의 의견이 기업의 성과에 어떻게 영향을 미치는 지에 대하여 확인하기 위한 것이다. 본 연구는 총3단계에 걸쳐서 진행되었습니다. 첫 번째 단계는 감성사전 구축의 단계로서 2013년 1월 1일부터 2015년 12월 31일까지 미국 내 26개의 자동차 제조 회사의 고객의 소리 (VOC: Voice of the Customer) 총 45,447개를 자동차 커뮤니티로부터 크롤링 (crawling)하여 POS (Part-of-Speech) 즉 품사정보를 추출하는 태깅 (tagging)과정을 거쳐 부정적, 긍정적 감성의 빈도수를 측정하여 감성사전을 구축하였고, 이에 대한 극성을 측정하여 7개의 감성도메인을 만들었습니다. 두 번째 단계는 데이터에 대한 신뢰성 분석의 단계로서 자기상관관계분석 (Auto-correlation Analysis)과 주성분분석 (PCA: Principal Component Analysis)을 통해 데이터가 실험에 적합한지를 검증하였다. 세 번째 단계에서는 2개의 선형회귀분석 모델로 7개의 감성영역이 미국내 자동차 제조 회사 중 GM, 포드, FCA, 폭스바겐 등 총 4개의 자동차 생산 기업을 선정하여 이들 기업의 성과 즉, 자동차 시장점유율에 어떤 영향을 미치고 있는 지 실험하였다. 그 결과, 우리는 4,815개의 부정적인 어휘들과 2,021개의 긍정적인 감성어휘들을 추출하여 감성사전을 구축하였으며, 구축된 감성사전을 바탕으로, 추출되고 분류된 부정적이고 긍정적인 어휘들을 자동차 산업에 관련된 어휘들과 조합하였고, 자기상관분석과 PCA (주성분 분석)를 통해 감성의 특성을 조사하였다. 실험 결과에 따르면, 자기상관분석에 의해서 감성 데이터에 어떤 일정한 패턴이 존재한다는 것이 발견되었고, 각각의 감성 영역의 감성이 자기상관성이 있으며, 감성의 시계열성 또한 관찰되었다. PCA에 의한 결과로서, 7개 감성영역이 부정성, 긍정성, 중립성을 주성분으로 연결되어 있음을 확인할 수 있었다. 자기상관분석과 PCA를 통한 VOC 감성 데이터에 대한 신뢰성을 바탕으로 2개의 선형회귀분석 모델을 구축하여 실험을 진행하였다. 첫 번째 모델은 주성분 분석에서 부정적 감성의 Sadness, Anger, Fear와 긍정적 감성도메인인 Delight, Satisfaction을 독립변수로 선정하고, 시장점유율을 종속변수로 선정하여 실행하였고 두 번째 모델은 첫 번째 모델에 주성분이 중립성으로 결과가 나온 Shame, Frustration을 독립변수에 추가하여 중립성을 띠고 있는 감성이 시장 점유율에 유의미한 영향을 미치고 있는 지를 확인하였다. 분석 결과, 각 기업 마다 시장점유율에 유의미한 영향을 미치는 감성들이 존재하고 모델 1과, 모델 2에서의 감성 영향력이 차이가 있음을 발견하였다. 본 연구를 통해, 데이터 상에 나타난 정보를 가진 감성이 과거 값에 기초하여 자동차 시장에서 변화를 수반할 수 있다는 것을 나타내고 있음을 확인하였다. 또한, 우리가 시장 데이터의 가용성을 적용하려고 할 때, 자동차 시장 관련 정보나 감성의 자기상관성을 잘 활용할 수 있다면, 감정 분석에 대한 연구에 큰 기여를 할 수 있을 뿐만 아니라, 실제 시장에서의 비지니스 성과에도 다양한 방법으로 기여할 수 있을 것으로 기대된다.List of Tables iv List of Figures v Abstract 1 1. Introduction 1.1 Back Ground 3 1.2 Necessity of Study 6 1.3 Purpose & Questions 8 1.4 Structure 9 2. Literature Reviews of VOC Analysis 2.1 Importance of VOC 11 2.2 Data Mining 15 2.2.1 Concept & Functionalities 15 2.2.2 Methodologies of Data mining 20 2.3 Text Mining 24 2.4 Sentiment Analysis 26 2.5 Research Trend in Korea 30 3. Methodology 3.1 Research Flow 32 3.2 Proposed Methodologies 34 3.2.1 Sentiment Analysis 34 3.2.2 Auto-correlation Analysis 37 3.2.3 Principal Component Analysis (PCA) 38 3.2.4 Linear Regression 40 4. Experiment & Analysis 4.1 Phase I: Constructing Sentiment Lexicon & 7 Sentiment Domains 43 4.1.1 The Subject of Analysis & Crawling Data 43 4.1.2 Extracting POS Information 44 4.1.3 Review Extracting POS Information 46 4.2 Phase II : Reliability Analysis 49 4.2.1 Auto-correlation Analysis of Sentiment 51 4.2.2 Principal Component Analysis of Sentiment 55 4.3 Phase III : Influence on Automotive Market Share 58 4.3.1 Linear Regression Model 58 4.3.2 Definition of Variables 60 4.3.3 The Result of Linear Regression Analysis 62 5. Conclusion 5.1 Summary of Study 73 5.2 Managerial Implication and Limitation 75 5.3 Future Study 77 References 79Docto

Predicting Precedent: A Psycholinguistic Artificial Intelligence in the Supreme Court

Since the proliferation of analytic methodologies and ‘big data’ in the 1980s, there have been multiple studies claiming to offer consistent predictions for Supreme Court behavior. Political scientists focus on analyzing the ideology of judges, with prediction accuracy as high as 70%. Institutionalists, such as Kaufmann (2019), seek to make predictions on verdicts based on a thorough, qualitative analysis of rules and structures, with predictive accuracy as high as 75%. We argue that a psycholinguistic model utilizing machine learning (SCOTUS_AI) can best predict Court outcomes. Extracting sentiment features from parsed briefs through the Linguistic Inquiry and Word Count (LIWC), our results indicate SCOTUS_AI (AUC = .8087; Top K=.9144) outcompetes traditional analysis in both class-controlled accuracy and range of possible, specific outcomes. Moreover, unlike traditional models, SCOTUS_AI can also predict the procedural outcome of the case as one-hot encoded by remand (AUC=.76). Our findings support a psycholinguistic paradigm of case analysis, suggesting that the framing of arguments is a relatively strong predictor of case results. Finally, we cast predictions for the Supreme Court docket, demonstrating that SCOTUS_AI can be practically deployed in the field for individual cases

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Dynamics under Uncertainty: Modeling Simulation and Complexity

The dynamics of systems have proven to be very powerful tools in understanding the behavior of different natural phenomena throughout the last two centuries. However, the attributes of natural systems are observed to deviate from their classical states due to the effect of different types of uncertainties. Actually, randomness and impreciseness are the two major sources of uncertainties in natural systems. Randomness is modeled by different stochastic processes and impreciseness could be modeled by fuzzy sets, rough sets, Dempster–Shafer theory, etc

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

LiveRetro: Visual Analytics for Strategic Retrospect in Livestream E-Commerce

Livestream e-commerce integrates live streaming and online shopping, allowing viewers to make purchases while watching. However, effective marketing strategies remain a challenge due to limited empirical research and subjective biases from the absence of quantitative data. Current tools fail to capture the interdependence between live performances and feedback. This study identified computational features, formulated design requirements, and developed LiveRetro, an interactive visual analytics system. It enables comprehensive retrospective analysis of livestream e-commerce for streamers, viewers, and merchandise. LiveRetro employs enhanced visualization and time-series forecasting models to align performance features and feedback, identifying influences at channel, merchandise, feature, and segment levels. Through case studies and expert interviews, the system provides deep insights into the relationship between live performance and streaming statistics, enabling efficient strategic analysis from multiple perspectives.Comment: Accepted by IEEE VIS 202

Mining social media data for biomedical signals and health-related behavior

Social media data has been increasingly used to study biomedical and health-related phenomena. From cohort level discussions of a condition to planetary level analyses of sentiment, social media has provided scientists with unprecedented amounts of data to study human behavior and response associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance, sentiment analysis especially for mental health, and other areas. We also discuss a variety of innovative uses of social media data for health-related applications and important limitations in social media data access and use.Comment: To appear in the Annual Review of Biomedical Data Scienc