Fast Sentiment Analysis Algorithm Based on Double Model Fusion
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.