Proceedings ArticleDOI
Prediction of Development Prospect of Electric Vehicles in China by Using Natural Language Processing
TLDR
In this article, the authors proposed a method to predict the sales of electric vehicles by analyzing people's comments on social media, where sentiment score, number of comments and likes, and keyword existence were treated as input indicators.Abstract:
It is believed that people's comments on a certain product may affect its sales condition. In this paper, we propose a method to predict the sales of electric vehicles by analyzing people's comments on social media. We scrap user comments from a Chinese social media “Weibo” and try to predict the electric vehicle sales in China by using Natural Language Processing (NLP). Sentiment score, number of comments and likes, and keyword existence are treated as input indicators. We test linear regression, random forest, and gradient boosting algorithm during the experiment. The result shows that the model which using gradient boosting algorithm to predict the market share of electric vehicles has the best performance.read more
Citations
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Proceedings ArticleDOI
Analyzing Public Opinion on Electrical Vehicles in Indonesia Using Sentiment Analysis and Topic Modeling
TL;DR: In this article , the authors discuss the implementation of sentiment analysis models through social media, Twitter, using supervised learning methods, such as Support Vector Machine (SVM), Logistic Regression, Random Forest, Gradient Boosting Algorithm, Convolutional Neural Networks (CNN), and Recurrent Neural Network (RNN).
Proceedings ArticleDOI
Analyzing Public Opinion on Electrical Vehicles in Indonesia Using Sentiment Analysis and Topic Modeling
TL;DR: In this paper , the authors discuss the implementation of sentiment analysis models through social media, Twitter, using supervised learning methods, such as Support Vector Machine (SVM), Logistic Regression, Random Forest, Gradient Boosting Algorithm, Convolutional Neural Networks (CNN), and Recurrent Neural Network (RNN).
References
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Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions
Ona Egbue,Suzanna Long +1 more
TL;DR: In this paper, the authors identify potential socio-technical barriers to consumer adoption of EVs and determine if sustainability issues influence consumer decision to purchase an EV, and provide valuable insights into preferences and perceptions of technology enthusiasts; individuals highly connected to technology development and better equipped to sort out the many differences between EVs and CVs.
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The influence of consumers' environmental beliefs and attitudes on energy saving behaviours
TL;DR: In this paper, the authors developed a conceptual framework of consumer environmental behavior and its antecedents, and test hypotheses within the framework by means of a survey of green consumers, finding that general environmental beliefs do influence norms on environmental actions and prices, but only norms on price are correlated with environmental attitudes.
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A long panel survey to elicit variation in preferences and attitudes in the choice of electric vehicles
TL;DR: In this paper, the authors studied the impact of real-life experience with electric vehicles (EVs) over a relatively long period of time on individual preferences and attitudes, and measured attitudinal effects that might affect the choice of an EV by individuals.
Journal ArticleDOI
Housing Price Prediction via Improved Machine Learning Techniques
TL;DR: This paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models and comprehensively validate multiple techniques in model implementation on regression and provide an optimistic result for housing price prediction.
Journal ArticleDOI
Using diffusion models to forecast market size in emerging markets with applications to the Chinese car market
Lixian Qian,Didier Soopramanien +1 more
TL;DR: In this paper, a novel approach that applies diffusion models using car ownership data to forecast car sales is proposed, where the authors demonstrate that marketing managers can use diffusion models to predict car sales in China where cars are new products to most consumers in this market.