Deep learning in Arabic sentiment analysis: An overview

2019 ◽  
pp. 016555151986548
Author(s):  
Amal Alharbi ◽  
Mounira Taileb ◽  
Manal Kalkatawi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
The Internet ◽  
Learning Approach ◽  
Comprehensive Overview ◽  
Exponential Increase ◽  
Arabic Sentiment Analysis

Sentiment analysis became a very motivating area in both academic and industrial fields due to the exponential increase of the online published reviews and recommendations. To solve the problem of analysing and classifying those reviews and recommendations, several techniques have been proposed. Lately, deep neural networks showed promising outcomes in sentiment analysis. The growing number of Arab users on the Internet along with the increasing amount of published Arabic reviews and comments encouraged researchers to apply deep learning to analyse them. This article is a comprehensive overview of research works that utilised the deep learning approach for Arabic sentiment analysis.

scholarly journals Assessment of bilateral knee pain from MR imaging using deep neural networks

2018 ◽  
Author(s):  
Gary H. Chang ◽  
David T. Felson ◽  
Shangran Qiu ◽  
Terence D. Capellini ◽  
Vijaya B. Kolachalama
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Neural Networks ◽  
Deep Learning ◽  
Knee Pain ◽  
Deep Neural Networks ◽  
Learning Approach ◽  
Mr Images ◽  
Bilateral Knee ◽  
Machine Learning Approach

ABSTRACTBackground and objectiveIt remains difficult to characterize pain in knee joints with osteoarthritis solely by radiographic findings. We sought to understand how advanced machine learning methods such as deep neural networks can be used to analyze raw MRI scans and predict bilateral knee pain, independent of other risk factors.MethodsWe developed a deep learning framework to associate information from MRI slices taken from the left and right knees of subjects from the Osteoarthritis Initiative with bilateral knee pain. Model training was performed by first extracting features from two-dimensional (2D) sagittal intermediate-weighted turbo spin echo slices. The extracted features from all the 2D slices were subsequently combined to directly associate using a fused deep neural network with the output of interest as a binary classification problem.ResultsThe deep learning model resulted in predicting bilateral knee pain on test data with 70.1% mean accuracy, 51.3% mean sensitivity, and 81.6% mean specificity. Systematic analysis of the predictions on the test data revealed that the model performance was consistent across subjects of different Kellgren-Lawrence grades.ConclusionThe study demonstrates a proof of principle that a machine learning approach can be applied to associate MR images with bilateral knee pain.SIGNIFICANCE AND INNOVATIONKnee pain is typically considered as an early indicator of osteoarthritis (OA) risk. Emerging evidence suggests that MRI changes are linked to pre-clinical OA, thus underscoring the need for building image-based models to predict knee pain. We leveraged a state-of-the-art machine learning approach to associate raw MR images with bilateral knee pain, independent of other risk factors.

scholarly journals Combining content and social features in a deep learning approach to Vietnamese email prioritization

2021 ◽  
Author(s):  
Ha Thanh Nguyen ◽  
Quan Dinh Dang ◽  
Anh Quang Tran
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
New Method ◽  
Learning Approach ◽  
Field Of Study ◽  
Learning Techniques ◽  
Email Overload ◽  
Social Features

The email overload problem has been discussed in numerous email-related studies. One of the possible solutions to this problem is email prioritization, which is the act of automatically predicting the importance levels of received emails and sorting the user’s inbox accordingly. Several learning-based methods have been proposed to address the email prioritization problem using content features as well as social features. Although these methods have laid the foundation works in this field of study, the reported performance is far from being practical. Recent works on deep neural networks have achieved good results in various tasks. In this paper, the authors propose a novel email prioritization model which incorporates several deep learning techniques and uses a combination of both content features and social features from email data. This method targets Vietnamese emails and is tested against a self-built Vietnamese email corpus. Conducted experiments explored the effects of different model configurations and compared the effectiveness of the new method to that of a previous work.

scholarly journals Sentiment Analysis about Smart Phones Using Twitter Corpus by Deep Learning Approach

2019 ◽  
Vol 8 (S2) ◽  
pp. 39-45
Author(s):  
R. Pavithra ◽  
A. R. Mohamed Shanavas
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Deep Learning ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Convolutional Neural Networks ◽  
Social Networking Sites ◽  
Smart Phones ◽  
Learning Approach ◽  
Human Beings

Micro blogging websites are nothing but social media website to which user makes quick and frequent posts. Twitter is one of the well-known micro blog sites which offer the space for person which can read and put up messages that are 148 characters in duration. Twitter messages also are referred to as Tweets. And will use these tweets as raw facts. Then use a way that automatically extracts tweets into advantageous, bad or neutral sentiments. By the usage of the sentiment evaluation the consumer can recognize the feedback about the product or services before make a purchase. The organization can use sentiment evaluation to know the opinion of clients about their products, so can examine customer pleasure and in line with that they could improve their product. Now-a-days social networking sites are at the growth, so massive amount of data is generated. Millions of human beings are sharing their views each day on micro blogging sites, since it includes short and simple expressions. In this thesis, able to discuss approximately a paradigm to extract the sentiment from a famous micro running a blog carrier, Twitter, wherein customers submit their opinions for the whole thing. And can use the deep mastering algorithm to categories the twitters which incorporates Convolutional Neural Networks. The experimental end result is presented to demonstrate the use and effectiveness of the proposed system.

scholarly journals Using deep neural networks to detect complex spikes of cerebellar Purkinje cells

2020 ◽  
Vol 123 (6) ◽  
pp. 2217-2234
Author(s):  
Akshay Markanday ◽  
Joachim Bellet ◽  
Marie E. Bellet ◽  
Junya Inoue ◽  
Ziad M. Hafed ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Purkinje Cell ◽  
Purkinje Cells ◽  
Deep Neural Networks ◽  
Learning Approach ◽  
Sorting Algorithms ◽  
Accuracy Level ◽  
Cerebellar Purkinje Cells ◽  
Complex Spikes

Purkinje cell “complex spikes,” fired at perplexingly low rates, play a crucial role in cerebellum-based motor learning. Careful interpretations of these spikes require manually detecting them, since conventional online or offline spike sorting algorithms are optimized for classifying much simpler waveform morphologies. We present a novel deep learning approach for identifying complex spikes, which also measures additional relevant neurophysiological features, with an accuracy level matching that of human experts yet with very little time expenditure.

scholarly journals Examining Attention Mechanisms in Deep Learning Models for Sentiment Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3883
Author(s):  
Spyridon Kardakis ◽  
Isidoros Perikos ◽  
Foteini Grivokostopoulou ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Neural Networks ◽  
Experimental Study ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks ◽  
Deep Neural Networks ◽  
Training Methods ◽  
Learning Models ◽  
Neural Models

Attention-based methods for deep neural networks constitute a technique that has attracted increased interest in recent years. Attention mechanisms can focus on important parts of a sequence and, as a result, enhance the performance of neural networks in a variety of tasks, including sentiment analysis, emotion recognition, machine translation and speech recognition. In this work, we study attention-based models built on recurrent neural networks (RNNs) and examine their performance in various contexts of sentiment analysis. Self-attention, global-attention and hierarchical-attention methods are examined under various deep neural models, training methods and hyperparameters. Even though attention mechanisms are a powerful recent concept in the field of deep learning, their exact effectiveness in sentiment analysis is yet to be thoroughly assessed. A comparative analysis is performed in a text sentiment classification task where baseline models are compared with and without the use of attention for every experiment. The experimental study additionally examines the proposed models’ ability in recognizing opinions and emotions in movie reviews. The results indicate that attention-based models lead to great improvements in the performance of deep neural models showcasing up to a 3.5% improvement in their accuracy.

scholarly journals A Deep Learning Approach for Malware and Software Piracy Threat Detection

2021 ◽  
Vol 11 (6) ◽  
pp. 7757-7762
Author(s):  
K. Aldriwish
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Software Piracy ◽  
Classification Performance ◽  
Learning Approach ◽  
Threat Detection ◽  
Deep Convolutional Neural Networks ◽  
Combined Model ◽  
Security Of Iot

Internet of Things (IoT) -based systems need to be up to date on cybersecurity threats. The security of IoT networks is challenged by software piracy and malware attacks, and much important information can be stolen and used for cybercrimes. This paper attempts to improve IoT cybersecurity by proposing a combined model based on deep learning to detect malware and software piracy across the IoT network. The malware’s model is based on Deep Convolutional Neural Networks (DCNNs). Apart from this, TensorFlow Deep Neural Networks (TFDNNs) are introduced to detect software piracy threats according to source code plagiarism. The investigation is conducted on the Google Code Jam (GCJ) dataset. The conducted experiments prove that the classification performance achieves high accuracy of about 98%.

scholarly journals Evaluation of anti-EGFR-iRGD recombinant protein with GOLD nanoparticles: synergistic effect on antitumor efficiency using optimized deep neural networks

RSC Advances ◽  
2019 ◽  
Vol 9 (34) ◽  
pp. 19261-19270 ◽  
Author(s):  
Aman Chandra Kaushik ◽  
Yan-Jing Wang ◽  
Xiangeng Wang ◽  
Ajay Kumar ◽  
Satya P. Singh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Gold Nanoparticles ◽  
Deep Learning ◽  
Recombinant Protein ◽  
Synergistic Effect ◽  
Deep Neural Networks ◽  
Learning Approach ◽  
Biochemical Pathway ◽  
Biological Circuits

NP screening through a deep learning approach against Anti-EGFR and validation through docking with AuNP. Biochemical pathway and simulation of AuNP with Anti-EGFR and further implementation in biological circuits.

scholarly journals Survey on Object Tracking using Deep Learning Paradigms

2020 ◽  
Vol 8 (4) ◽  
pp. 34-46
Author(s):  
Saad Alhuzami
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Tracking ◽  
Deep Neural Networks ◽  
Final Section ◽  
Comprehensive Overview ◽  
Convolutional Networks ◽  
Detection And Tracking ◽  
Continuous Sequence ◽  
Learning Techniques

The field of object tracking extends across different domains. It is a major key player in the field of image processing and pattern recognition. Object tracking is the process of tracking an object over a continuous sequence of image frames to determine over time the relative movements or changes.  With the massive advancements in the field of deep learning, the use of deep neural networks has risen due to their impressive accomplishments in object detection and tracking. In this Survey, the objective is to give a comprehensive overview of the recent attempts in the field of object tracking with a focus on the use of deep learning techniques and algorithms. The paper is divided into four sections; at first, we will give an overview of the recent work to highlight the techniques and methods which have been used in object tracking using deep learning. The second section focus is on the object tracking that uses convolutional networks techniques. The third section focuses on some of the recurrent neural networks to tack objects. The final section is concentrated on auto-encoders object tracking.

scholarly journals Mineral Identification Based on Deep Learning That Combines Image and Mohs Hardness

Minerals ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 506
Author(s):  
Xiang Zeng ◽  
Yancong Xiao ◽  
Xiaohui Ji ◽  
Gongwen Wang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
The Internet ◽  
Measuring Instruments ◽  
Communication Signals ◽  
Identification Methods ◽  
Intelligent Algorithms ◽  
Mohs Hardness ◽  
Mineral Identification

Mineral identification is an important part of geological analysis. Traditional identification methods rely on either the experience of the appraisers or the various measuring instruments, and the methods are either easily influenced by appraisers’ experience or require too much work. To solve the above problems, there are studies using image recognition and intelligent algorithms to identify minerals. However, current studies cannot identify many minerals, and the accuracy is low. To increase the number of identified minerals and accuracy, we propose a method that uses both mineral photo images and the Mohs hardness in deep neural networks to identify the minerals. The experimental results showed that the method can reach 90.6% top-1 accuracy and 99.6% top-5 accuracy for 36 common minerals. An app based on the model was implemented on smartphones with no need for accessing the internet and communication signals. Tested on 73 real mineral samples, the app achieved top-1 accuracy of 89% when the mineral image and hardness are both used and 71.2% when only the mineral image is used.

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