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sentiment analysis.as a Source of Gaining Competitive Advantage on the Electricity Markets

Journal of Electronic Science and Technology 2015年第3期13卷 229-236页

作者： Wioletta Soko?owska Tymoteusz Hossa Karol Fabisz Witold Abramowicz Mateusz Kubaczyk the Department of Information Systems Faculty of Informatics and Electronic Economy Poznan University of Economics

The electricity retail markets are evolving toward more competitive and customer-oriented. The deployment of smart meters and a wealth of new technologies create customers＇ eagerness for taking control of their electricity consumption. By being better-informed about the energy usage, people are encouraged to switch deals among existing suppliers or move to a new energy provider. Moreover, as customers are more socially interconnected, the Internet portals and social media become a place for discussion, comparison, and evaluation of the available offers. Unfortunately, in case of the energy sector there is a lack of understanding that such information, when taken into account and properly analyzed, can be a completely new and a powerful source of competitive advantage. In the paper, we introduce a solution that the use of quasi real-time automated sentiment analysis.on the energy suppliers and the relevant aspects of their offers may enable energy companies to adapt quickly to changing circumstances, prevent potential customer churn, and harness new business opportunities.

关键词： Customer attrition electricity retail market sentiment analysis.

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Investigation on the Chinese Text sentiment analysis.Based on Convolutional Neural Networks in Deep Learning

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Computers, Materials & Continua 2019年第3期58卷 697-709页

作者： Feng Xu Xuefen Zhang Zhanhong Xin Alan Yang School of Economics and Management Beijing University of Posts and TelecommunicationsBeijing100876China Smart City College Beijing Union UniversityBeijing100101China Amphenol Assemble Tech HoustonTX 77070USA.

Nowadays,the amount of wed data is increasing at a rapid speed,which presents a serious challenge to the web *** sentiment analysis.an important research topic in the area of natural language processing,is a crucial task in the web monitoring *** accuracy of traditional text sentiment analysis.methods might be degraded in dealing with mass *** learning is a hot research topic of the artificial intelligence in the recent *** now,several research groups have studied the sentiment analysis.of English texts using deep learning *** contrary,relatively few works have so far considered the Chinese text sentiment analysis.toward this *** this paper,a method for analyzing the Chinese text sentiment is proposed based on the convolutional neural network(CNN)in deep learning in order to improve the analysis.*** feature values of the CNN after the training process are nonuniformly *** order to overcome this problem,a method for normalizing the feature values is ***,the dimensions of the text features are optimized through ***,a method for updating the learning rate in the training process of the CNN is presented in order to achieve better *** results on the typical datasets indicate that the accuracy of the proposed method can be improved compared with that of the traditional supervised machine learning methods,e.g.,the support vector machine method.

关键词： Convolutional neural network(CNN) deep learning learning rate normalization sentiment analysis.

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