{"title":"基于语料库的大数据文本分析卷积神经网络","authors":"Wedjdane Nahilia, Kahled Rezega, Okba Kazara","doi":"10.37380/jisib.v9i2.469","DOIUrl":null,"url":null,"abstract":"Companies market their services and products on social media platforms with today's easy access to the internet. As result, they receive feedback and reviews from their users directly on their social media sites. Reading every text is time-consuming and resourcedemanding. With access to technology-based solutions, analyzing the sentiment of all these texts gives companies an overview of how positive or negative users are on specific subjects will minimize losses. In this paper, we propose a deep learning approach to perform sentiment analysis on reviews using a convolutional neural network model, because that they have proven remarkable results for text classification. We validate our convolutional neural network model using large-scale data sets: IMDB movie reviews and Reuters data sets with a final accuracy score of ~86% for both data sets.","PeriodicalId":43580,"journal":{"name":"Journal of Intelligence Studies in Business","volume":" ","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2019-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A new corpus-based convolutional neural network for big data text analytics\",\"authors\":\"Wedjdane Nahilia, Kahled Rezega, Okba Kazara\",\"doi\":\"10.37380/jisib.v9i2.469\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Companies market their services and products on social media platforms with today's easy access to the internet. As result, they receive feedback and reviews from their users directly on their social media sites. Reading every text is time-consuming and resourcedemanding. With access to technology-based solutions, analyzing the sentiment of all these texts gives companies an overview of how positive or negative users are on specific subjects will minimize losses. In this paper, we propose a deep learning approach to perform sentiment analysis on reviews using a convolutional neural network model, because that they have proven remarkable results for text classification. We validate our convolutional neural network model using large-scale data sets: IMDB movie reviews and Reuters data sets with a final accuracy score of ~86% for both data sets.\",\"PeriodicalId\":43580,\"journal\":{\"name\":\"Journal of Intelligence Studies in Business\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2019-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Intelligence Studies in Business\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37380/jisib.v9i2.469\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligence Studies in Business","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37380/jisib.v9i2.469","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS","Score":null,"Total":0}
A new corpus-based convolutional neural network for big data text analytics
Companies market their services and products on social media platforms with today's easy access to the internet. As result, they receive feedback and reviews from their users directly on their social media sites. Reading every text is time-consuming and resourcedemanding. With access to technology-based solutions, analyzing the sentiment of all these texts gives companies an overview of how positive or negative users are on specific subjects will minimize losses. In this paper, we propose a deep learning approach to perform sentiment analysis on reviews using a convolutional neural network model, because that they have proven remarkable results for text classification. We validate our convolutional neural network model using large-scale data sets: IMDB movie reviews and Reuters data sets with a final accuracy score of ~86% for both data sets.
期刊介绍:
The Journal of Intelligence Studies in Business (JISIB) is a double blinded peer reviewed open access journal published by Halmstad University, Sweden. Its mission is to help facilitate and publish original research, conference proceedings and book reviews. The journal includes articles within areas such as Competitive Intelligence, Business Intelligence, Market Intelligence, Scientific and Technical Intelligence, Collective Intelligence and Geo-economics. This means that the journal has a managerial as well as an applied technical side (Information Systems), as these are now well integrated in real life Business Intelligence solutions. By focusing on business applications the journal do not compete directly with journals of Library Sciences or State or Military Intelligence Studies. Topics within the selected study areas should show clear practical implications.