Social Recommendation for Social Networks Using Deep Learning Approach: A Systematic Review

2021 ◽  
pp. 15-29
Author(s):  
Muhammad Alrashidi ◽  
Ali Selamat ◽  
Roliana Ibrahim ◽  
Ondrej Krejcar
Keyword(s):  
Systematic Review ◽  
Social Networks ◽  
Deep Learning ◽  
Learning Approach ◽  
Social Recommendation

scholarly journals Twitter Sentiment Analysis Model

2021 ◽  
Vol 9 (VII) ◽  
pp. 296-302
Author(s):  
Pushkar Dubey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Approach ◽  
Analysis Model ◽  
Market Place ◽  
The Social

Social networks are the main resources to gather information about people’s opinion towards different topics as they spend hours daily on social media and share their opinion. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers’ perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers’ perceptions. .We use natural language processing and machine learning concepts to create a model for analysis . In this paper we are discussing how we can create a model for analysis of twittes which is trained by various nlp , machine learning and Deep learning Approach.

scholarly journals Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Mario Andrés Paredes-Valverde ◽  
Ricardo Colomo-Palacios ◽  
María del Pilar Salas-Zárate ◽  
Rafael Valencia-García
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Deep Learning ◽  
Public Opinion ◽  
Customer Satisfaction ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Learning Approach ◽  
Complete Dataset

Sentiment analysis is an important area that allows knowing public opinion of the users about several aspects. This information helps organizations to know customer satisfaction. Social networks such as Twitter are important information channels because information in real time can be obtained and processed from them. In this sense, we propose a deep-learning-based approach that allows companies and organizations to detect opportunities for improving the quality of their products or services through sentiment analysis. This approach is based on convolutional neural network (CNN) and word2vec. To determine the effectiveness of this approach for classifying tweets, we conducted experiments with different sizes of a Twitter corpus composed of 100000 tweets. We obtained encouraging results with a precision of 88.7%, a recall of 88.7%, and an F-measure of 88.7% considering the complete dataset.

A Deep Learning Approach to Modeling Temporal Social Networks on Reddit

2019 ◽  
Author(s):  
Wingyan Chung ◽  
Cagri Toraman ◽  
Yifan Huang ◽  
Mehul Vora ◽  
Jinwei Liu
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Learning Approach

A deep learning approach for semi-supervised community detection in Online Social Networks

2021 ◽  
pp. 107345
Author(s):  
Aniello De Santo ◽  
Antonio Galli ◽  
Vincenzo Moscato ◽  
Giancarlo Sperlì
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Community Detection ◽  
Online Social Networks ◽  
Learning Approach

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Author response for "Deep learning and reinforcement learning approach on microgrid"

2020 ◽  
Author(s):  
Kumar Chandrasekaran ◽  
Prabaakaran Kandasamy ◽  
Srividhya Ramanathan
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Author Response ◽  
Learning Approach
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