Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews

2018 ◽  
Vol 10 (8) ◽  
pp. 2163-2175 ◽  
Author(s):  
Mohammad Al-Smadi ◽  
Bashar Talafha ◽  
Mahmoud Al-Ayyoub ◽  
Yaser Jararweh
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Twitter Sentiment Analysis Classification in the Arabic Language using Long Short-Term Memory Neural Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 235-239
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Arabic Language ◽  
Short Term ◽  
Term Memory ◽  
User Comments ◽  
Long Short Term Memory ◽  
Use Of Social Media

The increasing use of social media and the idea of extracting meaningful expressions from renewable and usable data which is one of the basic principles of data mining has increased the popularity of Sentiment Analysis which is an important working area recently and has expanded its usage areas. Compiled messages shared from social media can be meaningfully labeled with sentiment analysis technique. Sentiment analysis objectively indicates whether the expression in a text is positive, neutral, or negative. Detecting Arabic tweets will help for politicians in estimating universal incident-based popular reports and people’s comments. In this paper, classification was conducted on sentiments twitted in the Arabic language. The fact that Arabic has twisted language features enabled it to have a morphologically rich structure. In this paper we have used the Long Short Term Memory (LSTM), a widely used type of the Recurrent Neural Networks (RNNs), to analyze Arabic twitter user comments. Compared to conventional pattern recognition techniques, LSTM has more effective results in terms of having less parameter calculation, shorter working time and higher accuracy.

scholarly journals Using Machine Learning Methods in the Financial Market for Technical Analysis Based on Hybrid Models

2020 ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Hybrid Model ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Input Structure ◽  
Long Short Term Memory

In this study, it is presented a new hybrid model based on deep neural networks to predict the direction and magnitude of the Forex market movement in the short term. The overall model presented is based on the scalping strategy and is provided for high frequency transactions. The proposed hybrid model is based on a combination of three models based on deep neural networks. The first model is a deep neural network with a multi-input structure consisting of a combination of Long Short Term Memory layers. The second model is a deep neural network with a multi-input structure made of a combination of one-dimensional Convolutional Neural network layers. The third model has a simpler structure and is a multi-input model of the Multi-Layer Perceptron layers. The overall model was also a model based on the majority vote of three top models. This study showed that models based on Long Short-Term Memory layers provided better results than the other models and even hybrid models with more than 70% accurate.

scholarly journals Perbandingan Model Deep Learning untuk Klasifikasi Sentiment Analysis dengan Teknik Natural Languange Processing

2021 ◽  
Vol 7 (2) ◽  
pp. 113-121
Author(s):  
Firman Pradana Rachman
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Setiap orang mempunyai pendapat atau opini terhadap suatu produk, tokoh masyarakat, atau pun sebuah kebijakan pemerintah yang tersebar di media sosial. Pengolahan data opini itu di sebut dengan sentiment analysis. Dalam pengolahan data opini yang besar tersebut tidak hanya cukup menggunakan machine learning, namun bisa juga menggunakan deep learning yang di kombinasikan dengan teknik NLP (Natural Languange Processing). Penelitian ini membandingkan beberapa model deep learning seperti CNN (Convolutional Neural Network), RNN (Recurrent Neural Networks), LSTM (Long Short-Term Memory) dan beberapa variannya untuk mengolah data sentiment analysis dari review produk amazon dan yelp.

Sentiment Analysis in the Light of LSTM Recurrent Neural Networks

2018 ◽  
Vol 9 (1) ◽  
pp. 33-39 ◽  
Author(s):  
Subarno Pal ◽  
Soumadip Ghosh ◽  
Amitava Nag
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Different Types ◽  
Long Short Term Memory ◽  
Increase In Accuracy ◽  
Temporal Sequences

Long short-term memory (LSTM) is a special type of recurrent neural network (RNN) architecture that was designed over simple RNNs for modeling temporal sequences and their long-range dependencies more accurately. In this article, the authors work with different types of LSTM architectures for sentiment analysis of movie reviews. It has been showed that LSTM RNNs are more effective than deep neural networks and conventional RNNs for sentiment analysis. Here, the authors explore different architectures associated with LSTM models to study their relative performance on sentiment analysis. A simple LSTM is first constructed and its performance is studied. On subsequent stages, the LSTM layer is stacked one upon another which shows an increase in accuracy. Later the LSTM layers were made bidirectional to convey data both forward and backward in the network. The authors hereby show that a layered deep LSTM with bidirectional connections has better performance in terms of accuracy compared to the simpler versions of LSTM used here.

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