scholarly journals Video kills the sentiment—Exploring fans’ reception of the video assistant referee in the English premier league using Twitter data

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242728
Author(s):  
Otto Kolbinger ◽  
Melanie Knopp
Keyword(s):  
Gradient Boosting ◽  
Support Vector ◽  
Classification Problems ◽  
Significant Drop ◽  
Text Corpora ◽  
Premier League ◽  
Evaluative Research ◽  
Negative Sentiment ◽  
The Impact ◽  
English Premier League

Evaluative research of technological officiating aids in sports predominantly focuses on the respective technology and the impact on decision accuracy, whereas the impact on stakeholders is neglected. Therefore, the aim of this study was to investigate the immediate impact of the recently introduced Video Assistant Referee, often referred to as VAR, on the sentiment of fans of the English Premier League. We analyzed the content of 643,251 tweets from 129 games, including 94 VAR incidents, using a new variation of a gradient boosting approach to train two tree-based classifiers for text corpora: one classifier to identify tweets related to the VAR and another one to rate a tweet’s sentiment. The results of 10-fold cross-validations showed that our approach, for which we only took a small share of all features to grow each tree, performed better than common approaches (naïve Bayes, support vector machines, random forest and traditional gradient tree boosting) used by other studies for both classification problems. Regarding the impact of the VAR on fans, we found that the average sentiment of tweets related to this technological officiating aid was significantly lower compared to other tweets (-0.64 vs. 0.08; t = 45.5, p < .001). Further, by tracking the mean sentiment of all tweets chronologically for each game, we could display that there is a significant drop of sentiment for tweets posted in the periods after an incident compared to the periods before. A plunge that persisted for 20 minutes on average. Summed up, our results provide evidence that the VAR effects predominantly expressions of negative sentiment on Twitter. This is in line with the results found in previous, questionnaire-based, studies for other technological officiating aids and also consistent with the psychological principle of loss aversion.

scholarly journals Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Arturo Moncada-Torres ◽  
Marissa C. van Maaren ◽  
Mathijs P. Hendriks ◽  
Sabine Siesling ◽  
Gijs Geleijnse
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Explicit Knowledge ◽  
Cox Regression ◽  
Metastatic Breast ◽  
Gradient Boosting ◽  
Support Vector ◽  
Netherlands Cancer Registry ◽  
Extreme Gradient Boosting ◽  
The Impact

AbstractCox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods, they are often disregarded because of their lack of transparency and little to no explainability, which are key for their adoption in clinical settings. In this paper, we used data from the Netherlands Cancer Registry of 36,658 non-metastatic breast cancer patients to compare the performance of CPH with ML techniques (Random Survival Forests, Survival Support Vector Machines, and Extreme Gradient Boosting [XGB]) in predicting survival using the $$c$$ c -index. We demonstrated that in our dataset, ML-based models can perform at least as good as the classical CPH regression ($$c$$ c -index $$\sim \,0.63$$ ∼ 0.63 ), and in the case of XGB even better ($$c$$ c -index $$\sim 0.73$$ ∼ 0.73 ). Furthermore, we used Shapley Additive Explanation (SHAP) values to explain the models’ predictions. We concluded that the difference in performance can be attributed to XGB’s ability to model nonlinearities and complex interactions. We also investigated the impact of specific features on the models’ predictions as well as their corresponding insights. Lastly, we showed that explainable ML can generate explicit knowledge of how models make their predictions, which is crucial in increasing the trust and adoption of innovative ML techniques in oncology and healthcare overall.

scholarly journals Exploring the Mechanism of Crashes with Autonomous Vehicles Using Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hengrui Chen ◽  
Hong Chen ◽  
Ruiyu Zhou ◽  
Zhizhen Liu ◽  
Xiaoke Sun
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Classification And Regression Tree ◽  
Gradient Boosting ◽  
Support Vector ◽  
Crash Severity ◽  
Apriori Algorithm ◽  
Driving Mode ◽  
Extreme Gradient Boosting ◽  
The Impact

The safety issue has become a critical obstacle that cannot be ignored in the marketization of autonomous vehicles (AVs). The objective of this study is to explore the mechanism of AV-involved crashes and analyze the impact of each feature on crash severity. We use the Apriori algorithm to explore the causal relationship between multiple factors to explore the mechanism of crashes. We use various machine learning models, including support vector machine (SVM), classification and regression tree (CART), and eXtreme Gradient Boosting (XGBoost), to analyze the crash severity. Besides, we apply the Shapley Additive Explanations (SHAP) to interpret the importance of each factor. The results indicate that XGBoost obtains the best result (recall = 75%; G-mean = 67.82%). Both XGBoost and Apriori algorithm effectively provided meaningful insights about AV-involved crash characteristics and their relationship. Among all these features, vehicle damage, weather conditions, accident location, and driving mode are the most critical features. We found that most rear-end crashes are conventional vehicles bumping into the rear of AVs. Drivers should be extremely cautious when driving in fog, snow, and insufficient light. Besides, drivers should be careful when driving near intersections, especially in the autonomous driving mode.

The impact of different parameter sets on the classification of asteroid types

2021 ◽  
Author(s):  
Hanna Klimczak ◽  
Wojciech Kotłowski ◽  
Dagmara Oszkiewicz ◽  
Francesca DeMeo ◽  
Agnieszka Kryszczyńska ◽  
...  
Keyword(s):  
Gradient Boosting ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Machine Learning Methods ◽  
Vector Machines ◽  
Science Centre ◽  
The Difference ◽  
The Impact

&lt;p&gt;The aim of the project is the classification of asteroids according to the most commonly used asteroid taxonomy (Bus-Demeo et al. 2009) with the use of various machine learning methods like Logistic Regression, Naive Bayes, Support Vector Machines, Gradient Boosting and Multilayer Perceptrons. Different parameter sets are used for classification in order to compare the quality of prediction with limited amount of data, namely the difference in performance between using the 0.45mu to 2.45mu spectral range and multiple spectral features, as well as performing the Prinicpal Component Analysis to reduce the dimensions of the spectral data.&lt;/p&gt; &lt;p&gt;&amp;#160;&lt;/p&gt; &lt;p&gt;This work has been supported by grant&amp;#160;No. 2017/25/B/ST9/00740 from the National Science Centre, Poland.&lt;/p&gt;

scholarly journals The impact of English Premier League broadcasts on Danish spectator demand: a small league perspective

2019 ◽  
Vol 89 (6) ◽  
pp. 633-653 ◽  
Author(s):  
Christian Gjersing Nielsen ◽  
Rasmus K. Storm ◽  
Tor Georg Jakobsen
Keyword(s):  
Premier League ◽  
The Impact ◽  
English Premier League

scholarly journals Sentiment Analysis of Impact of Technology on Employment from Text on Twitter

2020 ◽  
Vol 14 (07) ◽  
pp. 88
Author(s):  
Shahzad Qaiser ◽  
Nooraini Yusoff ◽  
Farzana Kabir Ahmad ◽  
Ramsha Ali
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Issues ◽  
Support Vector ◽  
Ripple Effect ◽  
Learning Classifier ◽  
The People ◽  
Impact Of Technology ◽  
Negative Sentiment ◽  
The Impact

Many different studies are in progress to analyze the content created by the users on social media due to its influence and social ripple effect. Various content created on social media has pieces of information and user’s sentiments about social issues. This study aims to analyze people’s sentiments about the impact of technology on employment and advancements in technologies and build a machine learning classifier to classify the sentiments. People are getting nervous, depressed and even doing suicides due to unemployment; hence, it is essential to explore this relatively new area of research. The study has two main objectives 1) to preprocess text collected from Twitter concerning the impact of technology on employment and analyze its sentiment, 2) to evaluate the performance of machine learning Naïve Bayes (NB) classifier on the text. To achieve this, a methodology is proposed that includes 1) data collection and preprocessing 2) analyze sentiment, 3) building machine learning classifier and 4) compare the performance of NB and support vector machine (SVM). NB and SVM achieved 87.18% and 82.05% accuracy respectively. The study found that 65% of the people hold negative sentiment regarding the impact of technology on employment and technological advancements; hence people must acquire new skills to minimize the effect of structural unemployment.

You don't know what you're doing! The impact of managerial change on club performance in the English Premier League

2014 ◽  
Vol 19 (6) ◽  
pp. 390-399 ◽  
Author(s):  
Stuart W. Flint ◽  
Daniel J. Plumley ◽  
Robert J. Wilson
Keyword(s):  
Premier League ◽  
The Impact ◽  
English Premier League

scholarly journals Spectator Demand, Uncertainty of Results, and Public Interest

2015 ◽  
Vol 19 (1) ◽  
pp. 3-30 ◽  
Author(s):  
Adam Cox
Keyword(s):  
Public Interest ◽  
Demand Uncertainty ◽  
Revenue Sharing ◽  
Potential Source ◽  
Premier League ◽  
The Impact ◽  
English Premier League ◽  
Uncertainty Of Results

This article tests the impact of match outcome uncertainty on stadium attendance and television audiences of English Premier League football. The method accounts for different measures of outcome uncertainty, an issue identified as a potential source of discord between existing evidence. Results show that more certain matches are preferred by spectators at the stadium yet more uncertain matches are preferred on TV spectators. Thus, a change in revenue sharing polices aimed at promoting a more uncertain match may affect both TV and stadium demand in opposing directions.

scholarly journals Predicting Suitable Habitats of Melia Azedarach L. Using Data Mining

2021 ◽  
Author(s):  
Lei Feng ◽  
Xiangni Tian ◽  
Yousry A. El-Kassaby ◽  
Jian Qiu ◽  
Ze Feng ◽  
...  
Keyword(s):  
Data Mining ◽  
Species Distribution ◽  
Mean Annual Precipitation ◽  
Gradient Boosting ◽  
Melia Azedarach ◽  
Support Vector ◽  
Suitable Habitat ◽  
Degree Days ◽  
Extreme Gradient Boosting ◽  
The Impact

Abstract Background: Melia azedarach L. is a globally distributed tree species of economic importance; however, it is unclear how the species distribution will respond to future climate changes.Methods: We aimed to select the most accurate one among seven data mining models to predict the species suitable contemporary and future habitats. These models include: maximum entropy (MaxEnt), support vector machine (SVM), generalized linear model (GLM), random forest (RF), naive bayesian model (NBM), extreme gradient boosting (XGBoost), and gradient boosting machine (GBM). A total of 906 M. azedarach locations were identified, and sixteen climate predictors were used for model building. The models’ validity was assessed using three measures (Area Under the Curves (AUC), kappa, and accuracy). Results: We found that the RF provided the most outstanding performance in prediction power and generalization capacity. The top climate factors affecting the species distribution were mean coldest month temperature (MCMT), followed by the number of frost-free days (NFFD), degree-days above 18°C (DD>18), temperature difference between MWMT and MCMT, or continentality (TD), mean annual precipitation (MAP), and degree-days below 18°C (DD<18). We projected that future suitable habitat of this species would increase under both the RCP4.5 and RCP8.5 scenarios for the 2020s, 2050s, and 2080s.Conclusion: Our findings are expected to assist in better understanding the impact of climate change on the species and provide scientific basis for its planting and conservation.

scholarly journals Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning

2020 ◽  
Vol 11 ◽  
Author(s):  
Liangxu Xie ◽  
Lei Xu ◽  
Ren Kong ◽  
Shan Chang ◽  
Xiaojun Xu
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Molecular Descriptor ◽  
Predictive Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Extreme Gradient Boosting ◽  
The Impact

The accurate predicting of physical properties and bioactivity of drug molecules in deep learning depends on how molecules are represented. Many types of molecular descriptors have been developed for quantitative structure-activity/property relationships quantitative structure-activity relationships (QSPR). However, each molecular descriptor is optimized for a specific application with encoding preference. Considering that standalone featurization methods may only cover parts of information of the chemical molecules, we proposed to build the conjoint fingerprint by combining two supplementary fingerprints. The impact of conjoint fingerprint and each standalone fingerprint on predicting performance was systematically evaluated in predicting the logarithm of the partition coefficient (logP) and binding affinity of protein-ligand by using machine learning/deep learning (ML/DL) methods, including random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), long short-term memory network (LSTM), and deep neural network (DNN). The results demonstrated that the conjoint fingerprint yielded improved predictive performance, even outperforming the consensus model using two standalone fingerprints among four out of five examined methods. Given that the conjoint fingerprint scheme shows easy extensibility and high applicability, we expect that the proposed conjoint scheme would create new opportunities for continuously improving predictive performance of deep learning by harnessing the complementarity of various types of fingerprints.

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