Semi-supervised Dual Recurrent Neural Network for Sentiment Analysis

2013 ◽  
Author(s):  
Wenge Rong ◽  
Baolin Peng ◽  
Yuanxin Ouyang ◽  
Chao Li ◽  
Zhang Xiong
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Recurrent Neural Network

scholarly journals SENTIMENT ANALYSIS OF ELECTRIC CARS USING RECURRENT NEURAL NETWORK METHOD IN INDONESIAN TWEETS

Kursor ◽  
2020 ◽  
Vol 10 (4) ◽  
Author(s):  
Felisia Handayani ◽  
Metty Mustikasari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Recurrent Neural Network ◽  
Confusion Matrix ◽  
Data Representation ◽  
Sequential Data ◽  
Communication Tool ◽  
Electric Cars

Sentiment analysis is computational research of the opinions of many people who are textually expressed against a particular topic. Twitter is the most popular communication tool among Internet users today to express their opinions. Deep Learning is a solution to allow computers to learn from experience and understand the world in terms of the hierarchy concept. Deep Learning objectives replace manual assignments with learning. The development of deep learning has a set of algorithms that focus on learning data representation. The recurrent Neural Network is one of the machine learning methods included in Deep learning because the data is processed through multi-players. RNN is also an algorithm that can recall the input with internal memory, therefore it is suitable for machine learning problems involving sequential data. The study aims to test models that have been created from tweets that are positive, negative, and neutral sentiment to determine the accuracy of the models. The models have been created using the Recurrent Neural Network when applied to tweet classifications to mark the individual classes of Indonesian-language tweet data sentiment. From the experiments conducted, results on the built system showed that the best test results in the tweet data with the RNN method using Confusion Matrix are with Precision 0.618, Recall 0.507 and Accuracy 0.722 on the data amounted to 3000 data and comparative data training and data testing of ratio data 80:20

Attention-based sentiment analysis using convolutional and recurrent neural network

2020 ◽  
Vol 113 ◽  
pp. 571-578
Author(s):  
Mohd Usama ◽  
Belal Ahmad ◽  
Enmin Song ◽  
M. Shamim Hossain ◽  
Mubarak Alrashoud ◽  
...  
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Recurrent Neural Network

scholarly journals Explaining Recurrent Neural Network Predictions in Sentiment Analysis

2017 ◽  
Author(s):  
Leila Arras ◽  
Grégoire Montavon ◽  
Klaus-Robert Müller ◽  
Wojciech Samek
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Recurrent Neural Network
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