Sentiment Classification using Attention based Gated-CNN with Deep Recurrent Neural Model

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Turkish Journal of Electrical Engineering and Computer Sciences Pub Date : 2021-01-01 DOI:10.3906/elk-1909-58
S. Rahman, Ashmita Riya, S. Haque
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Abstract

Sentiment analysis received a lot of attention recently due to its potential use in business intelligence. 4 Understanding variable length sentences to extract the sentimental context is the main challenge of this concept. Our 5 proposed models are moderations of a deep neural model named comprehensive attention recurrent model [5]. A new 6 layer of attention mechanism and replacement of LSTM with gated-CNN have been introduced to make learning of CA 7 model [5] faster and efficient. IMDB movie review sentiment-labelled dataset has been used in our experiments. Our 8 paper solely focuses on the comparison of performances among proposed and inspired models. Experimental results 9 imply that accuracy and precision of our proposed models are better compared to the state-of-the-art CA model. 10
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基于深度递归神经模型的关注门控cnn情感分类
由于情感分析在商业智能中的潜在应用,它最近受到了很多关注。理解可变长度的句子以提取情感语境是这个概念的主要挑战。我们提出的5个模型是深度神经模型综合注意循环模型[5]的调节。引入了一种新的6层注意机制,并将LSTM替换为gate - cnn,使ca7模型[5]的学习更快、更高效。在我们的实验中使用了IMDB电影评论情感标记数据集。我们的论文只关注于提出模型和启发模型之间的性能比较。实验结果9表明,与目前最先进的CA模型相比,我们提出的模型的准确性和精度都更好。10
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来源期刊
Turkish Journal of Electrical Engineering and Computer Sciences
Turkish Journal of Electrical Engineering and Computer Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
2.90
自引率
9.10%
发文量
95
审稿时长
6.9 months
期刊介绍: The Turkish Journal of Electrical Engineering & Computer Sciences is published electronically 6 times a year by the Scientific and Technological Research Council of Turkey (TÜBİTAK) Accepts English-language manuscripts in the areas of power and energy, environmental sustainability and energy efficiency, electronics, industry applications, control systems, information and systems, applied electromagnetics, communications, signal and image processing, tomographic image reconstruction, face recognition, biometrics, speech processing, video processing and analysis, object recognition, classification, feature extraction, parallel and distributed computing, cognitive systems, interaction, robotics, digital libraries and content, personalized healthcare, ICT for mobility, sensors, and artificial intelligence. Contribution is open to researchers of all nationalities.
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