文章基本信息

标题：Sentiment Analysis for Movies Reviews Dataset Using Deep Learning Models
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作者：Nehal Mohamed Ali ; Marwa Mostafa Abd El Hamid ; Aliaa Youssif 等
期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2019
卷号：9
期号：2/3
页码：19-27
DOI：10.5121/ijdkp.2019.9302
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Due to the enormous amount of data and opinions being produced, shared and transferred everyday across the internet and other media, Sentiment analysis has become vital for developing opinion mining systems. This paper introduces a developed classification sentiment analysis using deep learning networks and introduces comparative results of different deep learning networks. Multilayer Perceptron (MLP) was developed as a baseline for other networks results. Long short-term memory (LSTM) recurrent neural network, Convolutional Neural Network (CNN) in addition to a hybrid model of LSTM and CNN were developed and applied on IMDB dataset consists of 50K movies reviews files. Dataset was divided to 50% positive reviews and 50% negative reviews. The data was initially pre-processed using Word2Vec and word embedding was applied accordingly. The results have shown that, the hybrid CNN_LSTM model have outperformed the MLP and singular CNN and LSTM networks. CNN_LSTM have reported the accuracy of 89.2% while CNN has given accuracy of 87.7%, while MLP and LSTM have reported accuracy of 86.74% and 86.64 respectively. Moreover, the results have elaborated that the proposed deep learning models have also outperformed SVM, Na誰ve Bayes and RNTN that were published in other works using English datasets.
关键词：Deep learning; LSTM; CNN; Sentiment Analysis; Movies Reviews; Binary Classification