Fast Sentiment Analysis Algorithm Based on Double Model Fusion

作     者：Zhixing Lin Like Wang Xiaoli Cui Yongxiang Gu 

作者机构：Sanming UniversityNetwork CenterSanming365004 College of Mathematics and InformaticsFujian Normal UniversityFuzhou350007China Chengdu Institute of Computer ApplicationsChinese Academy of SciencesChengdu610041China University of Chinese Academy of SciencesBeijing100049China Sichuan Rainbow Consulting&Software Co.Ltd.Chengdu610041China 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2021年第36卷第1期

页      面：175-188页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Science Foundation of China(No.61771140) the 2017 Natural Science Foundation of Fujian Provincial Science&Technology Department(No.2018J01560) the 2016 Fujian Education and Scientific Research Project for Young and Middle-aged Teachers(JAT170522) 

主　　题：Sentiment analysis model fusion Bi-LSTM FastText 

摘      要：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 *** Chinese sentiment analysis algorithms cannot make full use of the order information in context and are inefficient in sentiment *** this paper,we systematically reviewed the classic and representative works in sentiment analysis and proposed a simple but efficient *** of all,FastText was trained to get the basic classification model,which can generate pre-trained word vectors as a ***,Bidirectional Long Short-Term Memory Network(Bi-LSTM)utilizes the generated word vectors for training and then merges with FastText to make comprehensive sentiment *** combining FastText and Bi-LSTM,we have developed a new fast sentiment analysis,called FAST-BiLSTM,which consistently achieves a balance between performance and *** particular,experimental results based on the real datasets demonstrate that our algorithm can effectively judge sentiments of users’comments,and is superior to the traditional algorithm in time efficiency,accuracy,recall and F1 criteria.