scholarly journals Examining Political Trustworthiness through Text-Based Measures of Ideology

2019 ◽  
Vol 33 ◽  
pp. 10029-10030
Author(s):  
Eitan Sapiro-Gheiler
Keyword(s):  
Machine Learning ◽  
Text Analysis ◽  
Statistical Data ◽  
Empirical Support ◽  
Policy Space ◽  
Ideological Position ◽  
One Dimensional ◽  
Word Level ◽  
The Us ◽  
Average Position

This work shows the value of word-level statistical data from the US Congressional Record for studying the ideological positions and dynamic behavior of senators. Using classification techniques from machine learning, we predict senators’ party with near-perfect accuracy. We also develop text-based ideology scores to embed a politician’s ideological position in a one-dimensional policy space. Using these scores, we find that speech that diverges from voting positions may result in higher vote totals. To explain this behavior, we show that politicians use speech to move closer to their party’s average position. These results not only provide empirical support for political economy models of commitment, but also add to the growing literature of machine-learning-based text analysis in social science contexts.

Using Machine Learning to Assess Yield Impacts of Crop Rotation: Combining Satellite and Statistical Data for Ukraine

2020 ◽  
Author(s):  
Klaus Deininger ◽  
Daniel Ayalew Ali ◽  
Nataliia Kussul ◽  
Mykola Lavreniuk ◽  
Oleg Nivievskyi
Keyword(s):  
Machine Learning ◽  
Crop Rotation ◽  
Statistical Data

scholarly journals A Machine Learning Approach as a Surrogate for a Finite Element Analysis: Status of Research and Application to One Dimensional Systems

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1654
Author(s):  
Poojitha Vurtur Badarinath ◽  
Maria Chierichetti ◽  
Fatemeh Davoudi Kakhki
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Finite Element Analysis ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Maintenance Scheduling ◽  
Element Analysis ◽  
One Dimensional ◽  
Artificial Neural

Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.

Predicting the price of Vietnamese shrimp products exported to the US market using machine learning

2021 ◽  
Author(s):  
Nguyen Minh Khiem ◽  
Yuki Takahashi ◽  
Khuu Thi Phuong Dong ◽  
Hiroki Yasuma ◽  
Nobuo Kimura
Keyword(s):  
Machine Learning ◽  
The Us

Summaries

2018 ◽  
Vol 46 (1) ◽  
Keyword(s):  
Machine Learning ◽  
Content Analysis ◽  
Text Analysis ◽  
English Language ◽  
Automated Analysis ◽  
Supervised Machine Learning ◽  
Automated Text Analysis ◽  
Research Problems ◽  
Dutch Language ◽  
Automated Methods

Damian Trilling & Jelle Boumans Automated analysis of Dutch language-based texts. An overview and research agenda While automated methods of content analysis are increasingly popular in today’s communication research, these methods have hardly been adopted by communication scholars studying texts in Dutch. This essay offers an overview of the possibilities and current limitations of automated text analysis approaches in the context of the Dutch language. Particularly in dictionary-based approaches, research is far less prolific as research on the English language. We divide the most common types of content-analytical research questions into three categories: 1) research problems for which automated methods ought to be used, 2) research problems for which automated methods could be used, and 3) research problems for which automated methods (currently) cannot be used. Finally, we give suggestions for the advancement of automated text analysis approaches for Dutch texts. Keywords: automated content analysis, Dutch, dictionaries, supervised machine learning, unsupervised machine learning

A content spectral-based text representation

2021 ◽  
pp. 1-12
Author(s):  
Melesio Crespo-Sanchez ◽  
Ivan Lopez-Arevalo ◽  
Edwin Aldana-Bobadilla ◽  
Alejandro Molina-Villegas
Keyword(s):  
Machine Learning ◽  
Text Analysis ◽  
Question Answering ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Text Representation ◽  
Feature Vectors ◽  
Learning Tasks ◽  
Semantic Component ◽  
Vector Representations

In the last few years, text analysis has grown as a keystone in several domains for solving many real-world problems, such as machine translation, spam detection, and question answering, to mention a few. Many of these tasks can be approached by means of machine learning algorithms. Most of these algorithms take as input a transformation of the text in the form of feature vectors containing an abstraction of the content. Most of recent vector representations focus on the semantic component of text, however, we consider that also taking into account the lexical and syntactic components the abstraction of content could be beneficial for learning tasks. In this work, we propose a content spectral-based text representation applicable to machine learning algorithms for text analysis. This representation integrates the spectra from the lexical, syntactic, and semantic components of text producing an abstract image, which can also be treated by both, text and image learning algorithms. These components came from feature vectors of text. For demonstrating the goodness of our proposal, this was tested on text classification and complexity reading score prediction tasks obtaining promising results.

Machine learning based text analysis for intelligence collation

2005 ◽  
Author(s):  
C. Thie ◽  
Z. Lock ◽  
D. Smith ◽  
E. Cribb ◽  
A. Ford ◽  
...  
Keyword(s):  
Machine Learning ◽  
Text Analysis

scholarly journals Analysis of computed discount rates for the largest companies of the mineral resources sector of Russia

2020 ◽  
Vol 177 ◽  
pp. 05006
Author(s):  
Vladimir Nikolaevich Podkorytov ◽  
Lyudmila Anatolyevna Mochalova
Keyword(s):  
Statistical Data ◽  
Mineral Resources ◽  
Securities Market ◽  
Capital Asset Pricing ◽  
Securities Markets ◽  
Discount Rates ◽  
Investment Projects ◽  
Asset Pricing Model ◽  
The Us ◽  
Capm Model

The article provides a comparative analysis of discount rates for the largest companies in the mineral resources sector of Russia, which are calculated on the basis of statistical data from the US and Russian securities markets. Using the CAPM model (Capital Asset Pricing Model) for each selected company, various ruble discount rates were obtained. Calculations based on statistical data from the Russian securities market showed higher rates, and this, according to the authors, can negatively affect the assessment of potential investment projects in terms of their effectiveness. According to the results of the study, it was concluded that when calculating discount rates, it is advisable to use statistical data from the US securities market, since they give more objective results. The appropriateness of their use in forecasting the return on investment is largely due to the length of the retrospective period when calculating the premium for the risk of investing in stocks (from 1928 to the present), smoothing out market volatility at certain crisis times. The Russian securities market has a short retrospective, uneven dynamics of indicators, which does not allow full use of its statistical information. The authors see the prospect of further research in constructing special stochastic models for discount rates forecasting to evaluate investments in companies of the mineral resources sector of Russia.

scholarly journals Natural Language Processing and Machine Learning for Detection of Respiratory Illness by Chest CT Imaging and Tracking of COVID-19 Pandemic in the US

2021 ◽  
Vol 3 (1) ◽  
pp. e200596
Author(s):  
Ricardo C. Cury ◽  
Istvan Megyeri ◽  
Tony Lindsey ◽  
Robson Macedo ◽  
Juan Batlle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Respiratory Illness ◽  
Ct Imaging ◽  
Chest Ct ◽  
The Us
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