Zhixing Lin^1,2, Like Wang^3,4, Xiaoli Cui⁵, Yongxiang Gu^3,4,*

Computer Systems Science and Engineering, Vol.36, No.1, pp. 175-188, 2021, DOI:10.32604/csse.2021.014260

Abstract Nowadays, as the number of textual data is exponentially increasing, sentiment analysis has become one of the most significant tasks in natural language processing (NLP) with increasing attention. Traditional Chinese sentiment analysis algorithms cannot make full use of the order information in context and are inefficient in sentiment inference. In this paper, we systematically reviewed the classic and representative works in sentiment analysis and proposed a simple but efficient optimization. First of all, FastText was trained to get the basic classification model, which can generate pre-trained word vectors as a by-product. Secondly, Bidirectional Long Short-Term Memory Network (Bi-LSTM) utilizes the… More >

Min Zhu^1,2,3,#, Huiyu Jin^1,#, Ruxue Chen¹, Quanyi Huang^2,3, Shaobo Zhong⁴, Guang Tian^1,*

Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1097-1109, 2020, DOI:10.32604/iasc.2020.010140

Abstract In a disaster, mass casualties lead to a surge in demand for medical services. Some relief actions have been criticized for being ill-adapted to dominating medical needs. This research established a disaster medical relief planning model in 3 steps. 1. Establishing the two-level conceptual model. 2. Using the ontology method to describe the hierarchy and relating rules of the terms and concepts associated with the model. 3. Using an ontology-support casebased reasoning approach to build the case similarity matching process, which can provide a more efficient system for decision support. A case study validated the model and demonstrated its usage. More >