A new corpus-based convolutional neural network for big data text analytics

Pub Date : 2019-11-13 DOI:10.37380/jisib.v9i2.469
Wedjdane Nahilia, Kahled Rezega, Okba Kazara
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引用次数: 3

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.
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基于语料库的大数据文本分析卷积神经网络
如今,随着互联网的普及,公司在社交媒体平台上营销其服务和产品。因此,他们直接在社交媒体网站上收到用户的反馈和评论。阅读每一篇文章既费时又耗费资源。通过使用基于技术的解决方案,分析所有这些文本的情绪,可以让公司了解用户对特定主题的积极或消极态度,从而最大限度地减少损失。在本文中,我们提出了一种深度学习方法,使用卷积神经网络模型对评论进行情绪分析,因为它们在文本分类方面已经证明了显著的结果。我们使用大规模数据集验证了我们的卷积神经网络模型:IMDB电影评论和路透社数据集,两个数据集的最终准确率得分均为~86%。
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