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

Journal of Intelligence Studies in Business

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Field Value
 
Title A new corpus-based convolutional neural network for big data text analytics
 
Creator Nahilia, Wedjdane
Rezega, Kahled
Kazara, Okba
 
Subject Marketing, Management, Information Systems
Convolutional neural networks, deep learning, natural language processing, NLP, user reviews, sentiment analysis, text classification
 
Description 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.
 
Publisher Journal of Intelligence Studies in Business
 
Contributor —
 
Date 2019-11-13
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
—
Empirical
 
Format application/pdf
 
Identifier https://ojs.hh.se/index.php/JISIB/article/view/469
 
Source Journal of Intelligence Studies in Business; Vol 9, No 2 (2019): Journal of Intelligence Studies in Business
2001-015X
2001-0168
 
Language eng
 
Relation https://ojs.hh.se/index.php/JISIB/article/view/469/215
 
Rights Copyright (c) 2019 Journal of Intelligence Studies in Business
http://creativecommons.org/licenses/by-nc-nd/4.0