Understanding Emojis for Financial Sentiment Analysis

2023 | International Conference on Information Systems | Citations: 0

Authors: Chen, Siyi; Xing, Frank

Abstract: ... Expand

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Topics: application programming interface social media website cryptocurrency Python

Methods: sentiment analysis machine learning BERT deep learning experiment

Prediction of hotel booking cancellations: Integration of machine learning and probability model based on interpretable feature interaction

2023 | Decision Support Systems | Citations: 0

Authors: Chen, Shuixia; Ngai, Eric W.T.; Ku, Yaoyao; Xu, Zeshui; Gou, Xunjie; Zhang, Chenxi

Abstract: Reliable hotel cancellation prediction can help establish appropriate operationa ... Expand

Abstract: Reliable hotel cancellation prediction can help establish appropriate operational strategies for hotel manage­ ment. In this sector, personal name records (PNR) data may be the most representative information source for prediction tasks. Despite the popularity of PNR, its inherent lack of availability has been commonly disregarded in the literature. Existing studies have directly input PNR into high-dimensional machine learning (ML) models to achieve cancellation predictions. Another type of model generates cancellation prediction based on the proba­ bility modeling of samples. In this study, we propose an interpretable feature interaction method to enrich the existing PNR information. Thereafter, we empirically assess the prediction performance of the two model classes. This study specifically determines whether or not the two methods can cross-fertilize each other to improve cancellation prediction. To do so, we propose a model integrating Bayesian networks (BNs) and Lasso regression for this prediction task. This study utilizes BNs for the probability model consistent with our correlated variables and dichotomous prediction setting. Moreover, we use a linear ML model (i.e., Lasso regression), given its ad­ vantages in reducing ineffective predictors and transparency for ranking feature importance. Empirical results show that the proposed integration model has better prediction performance, and the obtained BN estimators and interactive features are the most important predictors. This study contributes to the booking cancellation liter­ ature by proposing an interpretable feature interaction and a prediction method integrating two types of effective models. The obtained accurate and interpretable cancellation prediction further contributes to offering practical implications to hoteliers in managerial decision-making. Collapse

Topics: missing data decision making competitive advantage price management marketing management

Methods: lasso regression experiment computational algorithm robustness check parametric test

SOLVING THE DATA-DRIVEN NEWSVENDOR WITH ATTENTION TO TIME

2023 | European Conference On Information Systems | Citations: 0

Authors: Feddersen, Leif; Cleophas, Catherine

Abstract: Inventory management systems support firms in planning for an uncertain future ... Expand

Abstract: Inventory management systems support firms in planning for an uncertain future by using demand forecasts and optimization models to make restocking decisions. Recent work on the "data-driven" newsvendor found that incorporating machine learning (ML) can improve the success of inventory management by accounting for demand-driving information. However, ML methods are infamously hard to interpret, which may hinder their acceptance. To ameliorate this, we show how to apply an interpretable attention-based architecture, the Temporal Fusion Transformer (TFT), to the data-driven newsvendor problem. Our approach replicates and extends the original TFT time series forecasting method to the inventory management domain. We evaluate our method on two real-world retail datasets, each covering 260 perishable food items, and provide domain-specific benchmarks. The computational study illustrates TFT's interpretable predictions and their comparatively high accuracy. Our work aims to lay the groundwork for further design science research on transparency in human-AI collaboration in this domain. Collapse

Topics: human AI collaboration human computer interaction Python decision making accounting

Methods: time series analysis longitudinal research artificial neural network feature engineering machine learning

Theories: learning theory

Influence of Twitter on Hydroxychloroquine Medication Prescriptions for COVID-19 Patients

2023 | International Conference on Information Systems | Citations: 0

Authors: Gorthi, Sai Soundarya; Ganju, Kartik; Pinsonneault, Alain

Abstract: Social media is proposed to play a crucial role in healthcare providers' care d ... Expand

Abstract: Social media is proposed to play a crucial role in healthcare providers' care decisions. However, social media information may not always be reliable as false information is prone to virality, and emotional cues may drive information sharing. Hence, in our study, we strive to understand the influence of social media on healthcare providers' care decisions by empirically examining the influence of Twitter discourse regarding Hydroxychloroquine (HCQ) on the actual prescribing rates of the drug in the USA for treating Collapse

Topics: Twitter social media application programming interface Python missing data

Methods: longitudinal research machine learning qualitative coding autocorrelation analysis mixed method

Theories: elaboration likelihood model

Guidance in Business Intelligence & Analytics Systems: A Review and Research Agenda

2023 | European Conference On Information Systems | Citations: 0

Authors: Gunklach, Jonas; Nadj, Mario

Abstract: While the data amount grows exponentially, the number of people with analytical ... Expand

Abstract: While the data amount grows exponentially, the number of people with analytical and technical skills is only slowly increasing. This skill gap is putting pressure on the labor market and increasing the need for personnel with these skills. At the same time, companies are forced to think of alternative ways to empower their less-skilled workforce to take on Business Intelligence and Analytics (BI&A) tasks. One promising attempt to address these challenges may turn to the concept of guidance. However, the current body of research on guidance in BI&A systems is scattered and lacks a structured investigation from which future research avenues can be derived. To address this gap, this article analyzes five categories, namely BI&A phases, guidance degree, guidance generation, user roles, and interactivity form. Reviewing 82 articles, our contribution is to synopsize articles on guidance in BI&A systems and to suggest five research avenues. Collapse

Topics: analytical information system data visualization data quality business intelligence artificial intelligence

Methods: exploratory data analysis machine learning literature sample time series analysis longitudinal research

ARTIFICIAL INTELLIGENCE-AUGMENTED DECISION MAKING IN SUPPLY CHAIN MONITORING: AN ACTION DESIGN RESEARCH STUDY

2023 | European Conference On Information Systems | Citations: 0

Authors: Herath Pathirannehelage, Savindu; Jóhannsson, Jóhann; Shrestha, Yash; Krogh, Georg von

Abstract: Organizations are progressively adopting hybrid human-artificial intelligence ( ... Expand

Abstract: Organizations are progressively adopting hybrid human-artificial intelligence (AI) systems in decisionmaking processes, with human decisions being augmented by AI insights. Among the promising AI applications in supply chain monitoring (SCMo) are predictive maintenance systems that predict potential device failures and augment maintenance decisions, allowing for timely and efficient interventions. Despite the growing proliferation of such systems, prescriptive knowledge encompassing technical, business, and organizational aspects on how to design, develop, and deploy them in actual operational environments remains limited. To address this shortcoming of evidence-based design principles in practice, our action design research developed a predictive maintenance system that predicts SCMo device failures and augments the maintenance decisions about those devices. By doing so, we outline generalizable design principles to guide prospective predictive maintenance systems in SCMo. Collapse

Topics: artificial intelligence predictive maintenance organizational context decision making systems evaluation

Methods: machine learning design principle design artifact artificial neural network random forest classification

Theories: evidence-based design

Can Conversations on Reddit Forecast Future Economic Uncertainty? An Interpretable Machine Learning Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: Jiang, Jinhang; Feng, Mei; Srinivasan, Karthik

Abstract: In recent years, social media has become an indispensable source of information ... Expand

Abstract: In recent years, social media has become an indispensable source of information through which public attitudes, opinions, and concerns can be studied and quantified. This paper proposes an interpretable machine learning framework for predicting the Equity Market-related Economic Uncertainty Index using features generated from a popular discussion forum on Reddit. Our framework consists of a series of custom preprocessing and analytics methods, including BERTopic for latent topic identification and regularized linear models. Using our framework, we evaluate explanatory models with different configurations over a large corpus of Reddit posts belonging to the personal finance category. Our analysis generates valuable insights about discussion topics on Reddit and their efficacy in accurately predicting future economic uncertainty. The study demonstrates the potential of using social media data and interpretable machine learning to inform economic forecasting research. Collapse

Topics: Reddit social media explainable artificial intelligence Python permutation feature importance

Methods: descriptive statistic time series analysis longitudinal research topic model experiment

Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster

2023 | Information Systems Frontiers | Citations: 0

Authors: Kumar, Abhinav; Singh, Jyoti Prakash; Rana, Nripendra P.; Dwivedi, Yogesh K.

Abstract: During a disaster, a large number of disaster-related social media posts are wid ... Expand

Abstract: During a disaster, a large number of disaster-related social media posts are widely disseminated. Only a small percentage of disaster-related information is posted by eyewitnesses. The post of a disaster eyewitness offers an accurate depiction of the disaster. Therefore, the information posted by the eyewitness is preferred over the other source of information as it is more effective at helping organize rescue and relief operations and potentially saving lives. In this work, we propose a multi-channel convolutional neural network (MCNN) that uses three different word-embedding vectors together to classify disaster-related tweets into eyewitness, non-eyewitness, and don’t know classes. We compared the performance of the proposed multi-channel convolutional neural network with several attention-based deep-learning models and conventional machine learning-models such as recurrent neural network, gated recurrent unit, Long-Short-Term-Memory, convolutional neural network, logistic regression, support vector machine, and gradient boosting. The proposed multi-channel convolutional neural network achieved an F1-score of 0.84, 0.88, 0.84, and 0.86 with four disaster-related datasets of floods, earthquakes, hurricanes, and wildfires, respectively. The experimental results show that the training MCNN model with different word embedding together performs better than the conventional machine-learning models and several other deep-learning models. Collapse

Topics: Twitter earthquake hurricane crowdsourcing social media

Methods: machine learning deep learning convolutional neural network experiment GloVe

Between Attention and Portfolio Adjustment: Insights from Machine Learning-based Risk Preference Assessment

2023 | International Conference on Information Systems | Citations: 0

Authors: Li, Xin; Rai, Arun; Song, Qingping; Xu, Sean Xin

Abstract: Financial firms recommend products to customers, intending to gain their attent ... Expand

Abstract: Financial firms recommend products to customers, intending to gain their attention and change their portfolios. Based on behavioral decision-making theory, we argue attention's effect on portfolio adjustment is through the risk deviation between portfolio risk and their risk preference. Thus, to fully understand the adjustment process, it is necessary to assess customers' risk preferences. In this study, we use machine learning methods to measure customers' risk preferences. Then, we build a dynamic adjustment model and find that attention's impact on portfolio adjustment speed is stronger when customers' risk preference is higher than portfolio risk (which needs an upward adjustment) and when customers' risk preference is within historical portfolio risk experience. We conducted a field experiment and found that directing customers' attention to products addressing the risk deviation would lead to more portfolio adjustment activities. Our study illustrates the role of machine learning in enhancing our understanding of financial decision-making. Collapse

Topics: decision making analytical information system electronic finance web page online banking

Methods: field experiment experimental group machine learning XGBoost digital trace data

Training Machine Learning Models with Simulated Data for Improved Line Fault Events Classification From 3-Phase PMU Field Recordings

2023 | HICSS | Citations: 0

Authors: Otudi, H.; Mohamed, T.; Kezunovic, M.; Hu, Y.; Obradovic, Z.

Abstract: Utilizing data analytics and machine learning (ML) on phasor measurement units ... Expand

Abstract: Utilizing data analytics and machine learning (ML) on phasor measurement units (PMUs) data to analyze faults automatically is the focus of this paper. Insufficient labels and natural uneven distribution of different types of line fault events found in fieldrecorded PMU data make supervised ML model development challenging. To address this issue, we train off-the-shelf Support Vector Machine (SVM) ML models for line fault classification using simulated PMU data obtained from a combination of 12 physical and virtual PMUs placed on a synthetic IEEE 14-bus system as well as using this simulated data unified with field recordings. A conducted sensitivity study is focused on three factors, 1) the number of PMUs used to train the ML model, 2) the voltage level at which the model is trained, and 3) the vicinity of PMUs to transmission line faults. The ML models trained with simulated and field data are evaluated on one-year field-recorded data collected from 38 PMUs sparsely located in the US Western interconnection. We demonstrate that when training ML models with only simulated data, the performance varies significantly with different number of PMUs, voltage level, and PMU placement in separate areas of the synthetic grid (F1 score of 0.78 to 0.92). We obtained an F1 score of 0.94 using the simulated dataset integrated with field recordings. The performance of a ML model developed using simulated data is also evaluated on the three-phase voltage signals extracted from 188 PMUs in the Eastern interconnection accompanied by imprecise labels, where the majority of the labels do not identify the fault type. On this extremely challenging task, we achieved 77% accuracy solely using synthetic data for ML training. Collapse

Topics: Apache Spark analytical information system computer hardware

Methods: simulation machine learning support vector machine experiment event log