The Cross-Sectional Pricing of Corporate Bonds Using Big Data and Machine Learning

2020 ◽  
Author(s):  
Turan G. Bali ◽  
Amit Goyal ◽  
Dashan Huang ◽  
Fuwei Jiang ◽  
Quan Wen
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Corporate Bonds ◽  
Cross Sectional ◽  
The Cross

scholarly journals Distributed Supervised Sentiment Analysis of Tweets: Integrating Machine Learning and Streaming Analytics for Big Data Challenges in Communication and Audience Research

2019 ◽  
pp. 113 ◽  
Author(s):  
Carlos Arcila Calderón ◽  
Félix Ortega Mohedano ◽  
Mateo Álvarez ◽  
Miguel Vicente Mariño
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Sentiment Analysis ◽  
Real Time ◽  
Large Scale ◽  
Apache Spark ◽  
Audience Research ◽  
Distributed Environment ◽  
Cross Sectional ◽  
Large Scale Analysis

The large-scale analysis of tweets in real-time using supervised sentiment analysis depicts a unique opportunity for communication and audience research. Bringing together machine learning and streaming analytics approaches in a distributed environment might help scholars to obtain valuable data from Twitter in order to immediately classify messages depending on the context with no restrictions of time or storage, empowering cross-sectional, longitudinal and experimental designs with new inputs. Even when communication and audience researchers begin to use computational methods, most of them remain unfamiliar with distributed technologies to face big data challenges. This paper describes the implementation of parallelized machine learning methods in Apache Spark to predict sentiments in real-time tweets and explains how this process can be scaled up using academic or commercial distributed computing when personal computers do not support computations and storage. We discuss the limitation of these methods and their implications in communication, audience and media studies.El análisis a gran escala de tweets en tiempo real utilizando el análisis de sentimiento supervisado representa una oportunidad única para la investigación de comunicación y audiencias. El poner juntos los enfoques de aprendizaje automático y de analítica en tiempo real en un entorno distribuido puede ayudar a los investigadores a obtener datos valiosos de Twitter con el fin de clasificar de forma inmediata mensajes en función de su contexto, sin restricciones de tiempo o almacenamiento, mejorando los diseños transversales, longitudinales y experimentales con nuevas fuentes de datos. A pesar de que los investigadores de comunicación y audiencias ya han comenzado a utilizar los métodos computacionales en sus rutinas, la mayoría desconocen el uso de las tecnologías de computo distribuido para afrontar retos de dimensión big data.  Este artículo describe la implementación de métodos de aprendizaje automático paralelizados en Apache Spark para predecir sentimientos de tweets en tiempo real y explica cómo este proceso puede ser escalado usando computación distribuida tanto comercial como académica, cuando los ordenadores personales son insuficientes para almacenar y analizar los datos. Se discuten las limitaciones de estos métodos y sus implicaciones en los estudios de medios, comunicación y audiencias.

scholarly journals Digital conversations about depression among Hispanics and non-Hispanics in the US: a big‐data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ruby Castilla-Puentes ◽  
Anjali Dagar ◽  
Dinorah Villanueva ◽  
Laura Jimenez-Parrado ◽  
Liliana Gil Valleta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Open Source ◽  
Cross Sectional Study ◽  
Source Analysis ◽  
Learning Technology ◽  
Cross Sectional ◽  
The Us ◽  
Negative Tone ◽  
Online Conversation

Abstract Background Digital conversations can offer unique information into the attitudes of Hispanics with depression outside of formal clinical settings and help generate useful information for medical treatment planning. Our study aimed to explore the big data from open‐source digital conversations among Hispanics with regard to depression, specifically attitudes toward depression comparing Hispanics and non-Hispanics using machine learning technology. Methods Advanced machine‐learning empowered methodology was used to mine and structure open‐source digital conversations of self‐identifying Hispanics and non-Hispanics who endorsed suffering from depression and engaged in conversation about their tone, topics, and attitude towards depression. The search was limited to 12 months originating from US internet protocol (IP) addresses. In this cross-sectional study, only unique posts were included in the analysis and were primarily analyzed for their tone, topic, and attitude towards depression between the two groups using descriptive statistical tools. Results A total of 441,000 unique conversations about depression, including 43,000 (9.8%) for Hispanics, were posted. Source analysis revealed that 48% of conversations originated from topical sites compared to 16% on social media. Several critical differences were noted between Hispanics and non-Hispanics. In a higher percentage of Hispanics, their conversations portray “negative tone” due to depression (66% vs 39% non-Hispanics), show a resigned/hopeless attitude (44% vs. 30%) and were about ‘living with’ depression (44% vs. 25%). There were important differences in the author's determined sentiments behind the conversations among Hispanics and non-Hispanics. Conclusion In this first of its kind big data analysis of nearly a half‐million digital conversations about depression using machine learning, we found that Hispanics engage in an online conversation about negative, resigned, and hopeless attitude towards depression more often than non-Hispanic.

On the cross-sectional shape of the cellulose crystallites in the tunicate Halocynthia papillosa

1990 ◽  
Vol 48 (3) ◽  
pp. 566-567
Author(s):  
J.-F. Revol ◽  
Y. Van Daele ◽  
F. Gaill
Keyword(s):  
Contrast Imaging ◽  
Animal Kingdom ◽  
Bright Field ◽  
Field Mode ◽  
Cross Sectional ◽  
Ultrathin Sections ◽  
The Cross ◽  
Sectional Shape ◽  
Valonia Ventricosa ◽  
C Nmr

The only form of cellulose which could unequivocally be ascribed to the animal kingdom is the tunicin that occurs in the tests of the tunicates. Recently, high-resolution solid-state l3C NMR revealed that tunicin belongs to the Iβ form of cellulose as opposed to the Iα form found in Valonia and bacterial celluloses. The high perfection of the tunicin crystallites led us to study its crosssectional shape and to compare it with the shape of those in Valonia ventricosa (V.v.), the goal being to relate the cross-section of cellulose crystallites with the two allomorphs Iα and Iβ.In the present work the source of tunicin was the test of the ascidian Halocvnthia papillosa (H.p.). Diffraction contrast imaging in the bright field mode was applied on ultrathin sections of the V.v. cell wall and H.p. test with cellulose crystallites perpendicular to the plane of the sections. The electron microscope, a Philips 400T, was operated at 120 kV in a low intensity beam condition.

A Technique for Measuring the Cross-Sectional Axes of the Longissimus Dorsi Muscle and Fat Depth in Lambs1,2

1960 ◽  
Vol 19 (3) ◽  
pp. 803-809
Author(s):  
D. J. Matthews ◽  
R. A. Merkel ◽  
J. D. Wheat ◽  
R. F. Cox
Keyword(s):  
Longissimus Dorsi ◽  
Cross Sectional ◽  
Longissimus Dorsi Muscle ◽  
Dorsi Muscle ◽  
The Cross

Automated Diagonal Slice and View Solution for 3D Device Structure Analysis

2018 ◽  
Author(s):  
Sang Hoon Lee ◽  
Jeff Blackwood ◽  
Stacey Stone ◽  
Michael Schmidt ◽  
Mark Williamson ◽  
...  
Keyword(s):  
Cross Section ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Image Data ◽  
Planar Surface ◽  
Device Structure ◽  
Cross Sectional ◽  
Device Structures ◽  
The Cross ◽  
Planar Analysis

Abstract The cross-sectional and planar analysis of current generation 3D device structures can be analyzed using a single Focused Ion Beam (FIB) mill. This is achieved using a diagonal milling technique that exposes a multilayer planar surface as well as the cross-section. this provides image data allowing for an efficient method to monitor the fabrication process and find device design errors. This process saves tremendous sample-to-data time, decreasing it from days to hours while still providing precise defect and structure data.

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