Zhixuan Zhang

Bio: Zhixuan Zhang is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Maglev. The author has an hindex of 3, co-authored 3 publications receiving 38 citations.

A word-building method based on neural network for text classification

Abstract: Text classification is a foundational task in many natural language processing applications. All traditional text classifiers take words as the basic units and conduct the pre-training process (lik...

An Individual Evolutionary Game Model Guided by Global Evolutionary Optimization for Vehicle Energy Station Distribution

Abstract: Collective decision-making problems consisting of individual decisions are commonly seen in social applications. In this article, the vehicle energy station distribution problem (VESDP) is considered, which is modeled as a network-based collective decision-making problem fulfilling consumers’ requirements by arranging the distribution of energy stations rationally. This problem involves the game among the government and energy station investors. The government intends to maximize the satisfaction of both gas and electric vehicle (EV) customers through policy guidance, while investors aim to maximize their own profits. To solve this problem, we propose an individual evolutionary game model guided by global evolutionary optimization with the following three features. From the individual perspective, we use a network-based evolutionary game with a confidence mechanism to describe the behavior of investors. From the global perspective, we design a genetic algorithm to find out the global-optimized program, which considers the satisfaction of all customers. To heal the divergence between these two perspectives, we design a policy formulation method for the government to motivate selfish investors to adopt strategies in accordance with the overall interests of all customers by using subsidies and taxation. Experiments are performed on both square grid and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.

Carrying out consensual Group Decision Making processes under social networks using sentiment analysis over comparative expressions

Abstract: Social networks are the most preferred mean for the people to communicate. Therefore, it is quite usual that experts use them to carry out Group Decision Making processes. One disadvantage that recent Group Decision Making methods have is that they do not allow the experts to use free text to express themselves. On the contrary, they force them to follow a specific user–computer communication structure. This is against social network nature where experts are free to express themselves using their preferred text structure. This paper presents a novel model for experts to carry out Group Decision Making processes using free text and alternatives pairwise comparisons. The main advantage of this method is that it is designed to work using social networks. Sentiment analysis procedures are used to analyze free texts and extract the preferences that the experts provide about the alternatives. Also, our method introduces two ways of applying consensus measures over the Group Decision Making process. They can be used to determine if the experts agree among them or if there are different postures. This way, it is possible to promote the debate in those cases where consensus is low.

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

Abstract: In the last decade, sentiment analysis has been widely applied in many domains, including business, social networks and education. Particularly in the education domain, where dealing with and processing students’ opinions is a complicated task due to the nature of the language used by students and the large volume of information, the application of sentiment analysis is growing yet remains challenging. Several literature reviews reveal the state of the application of sentiment analysis in this domain from different perspectives and contexts. However, the body of literature is lacking a review that systematically classifies the research and results of the application of natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. In this article, we present the results of a systematic mapping study to structure the published information available. We used a stepwise PRISMA framework to guide the search process and searched for studies conducted between 2015 and 2020 in the electronic research databases of the scientific literature. We identified 92 relevant studies out of 612 that were initially found on the sentiment analysis of students’ feedback in learning platform environments. The mapping results showed that, despite the identified challenges, the field is rapidly growing, especially regarding the application of DL, which is the most recent trend. We identified various aspects that need to be considered in order to contribute to the maturity of research and development in the field. Among these aspects, we highlighted the need of having structured datasets, standardized solutions and increased focus on emotional expression and detection.