"Big Data Text Mining Analysis of Chinese Electric Vehicle Design -Focusing on the Chinese Market-"

2020 ◽  
Vol 23 (4) ◽  
pp. 69-77
Author(s):  
Lei Sun ◽  
Sang Young Lee
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Electric Vehicle ◽  
Vehicle Design ◽  
Chinese Market

Manufacturing the Electric Vehicle: A Window of Technological Opportunity for Southern California

1995 ◽  
Vol 27 (6) ◽  
pp. 835-862 ◽  
Author(s):  
C O Quandt
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
The United States ◽  
Vehicle Design ◽  
Development Programs ◽  
Technological Opportunity ◽  
Public And Private ◽  
Vehicle Technology ◽  
Automobile Manufacturer ◽  
Vehicle Technologies

The California Air Resources Board has mandated that by 1998 2% of new vehicles sold in California must be zero emission, effectively, electric vehicles. This requirement is largely responsible for the electric vehicle development programs run by almost every global automobile manufacturer that does business in the United States. At present, no single electric vehicle technology, from battery type, to propulsion system, to vehicle design, represents a standard for a protoelectric vehicle industry. In this paper competing electric vehicle technologies are reviewed, leading public and private electric vehicle research programs worldwide are summarized, and the barriers faced by competing technological systems in terms of manufacturing and infrastructural requirements are examined.

Sentiment Analysis with Text Mining in Contexts of Big Data

2017 ◽  
Vol 13 (3) ◽  
pp. 47-67 ◽  
Author(s):  
Carina Sofia Andrade ◽  
Maribel Yasmina Santos
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Decision Making Process ◽  
Practical Applications ◽  
Information Models ◽  
Evolution Of Technology ◽  
The Common ◽  
Analyse Data

The evolution of technology, along with the common use of different devices connected to the Internet, provides a vast growth in the volume and variety of data that are daily generated at high velocity, phenomenon commonly denominated as Big Data. Related with this, several Text Mining techniques make possible the extraction of useful insights from that data, benefiting the decision-making process across multiple areas, using the information, models, patterns or tendencies that these techniques are able to identify. With Sentiment Analysis, it is possible to understand which sentiments and opinions are implicit in this data. This paper proposes an architecture for Sentiment Analysis that uses data from the Twitter, which is able to collect, store, process and analyse data on a real-time fashion. To demonstrate its utility, practical applications are developed using real world examples where Sentiment Analysis brings benefits when applied. With the presented demonstration case, it is possible to verify the role of each used technology and the techniques adopted for Sentiment Analysis.

scholarly journals SUPPORTING AIR TRANSPORT POLICIES USING BIG DATA ANALYTICS: A DESCRIPTIVE APPROACH BASED EMERGING TREND ANALYSIS

2017 ◽  
Vol 8 (1) ◽  
pp. 51-72
Author(s):  
Jin-seo Park
Keyword(s):  
Qualitative Research ◽  
Big Data ◽  
Text Mining ◽  
Research Methods ◽  
Qualitative Research Methods ◽  
Research Papers ◽  
Emerging Trends ◽  
The Future ◽  
Descriptive Approach ◽  
Core Issues

Qualitative research methods based on literature review or expert judgement have been used to find core issues, analyze emerging trends and discover promising areas for the future. Deriving results from large amounts of information under this approach is both costly and time consuming. Besides, there is a risk that the results may be influenced by the subjective opinion of experts. In order to make up for such weaknesses, the analysis paradigm for choosing future emerging trend is undergoing a shift toward mplementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The hange used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and the promising areas for the future from research papers pertaining to overall aviation areas through text mining method, which is one of the big data analysis techniques. This study has limitations in that its analysis for retrieving the aviation-related core issues and promising fields was restricted to research papers containing the keyword "aviation." However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and emerging trends regarding the promising areas for the future in the aviation industry through the application of a big data-based descriptive approach.

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