Recurrent neural network with pooling operation and attention mechanism for sentiment analysis: A multi-task learning approach

2020 ◽  
Vol 203 ◽  
pp. 105856 ◽  
Author(s):  
Yi Cai ◽  
Qingbao Huang ◽  
Zejun Lin ◽  
Jingyun Xu ◽  
Zhenhong Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Recurrent Neural Network ◽  
Attention Mechanism ◽  
Learning Approach ◽  
Task Learning

scholarly journals Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors

2018 ◽  
Vol 10 (9) ◽  
pp. 3313 ◽  
Author(s):  
Qin Li ◽  
Shaobo Li ◽  
Jie Hu ◽  
Sen Zhang ◽  
Jianjun Hu
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Sentiment Analysis ◽  
Recurrent Neural Network ◽  
Topic Model ◽  
Online Reviews ◽  
Attention Mechanism ◽  
Sentiment Classification ◽  
Vector Representation ◽  
Gated Recurrent Unit

Sentiment analysis of online tourist reviews is playing an increasingly important role in tourism. Accurately capturing the attitudes of tourists regarding different aspects of the scenic sites or the overall polarity of their online reviews is key to tourism analysis and application. However, the performances of current document sentiment analysis methods are not satisfactory as they either neglect the topics of the document or do not consider that not all words contribute equally to the meaning of the text. In this work, we propose a bidirectional gated recurrent unit neural network model (BiGRULA) for sentiment analysis by combining a topic model (lda2vec) and an attention mechanism. Lda2vec is used to discover all the main topics of review corpus, which are then used to enrich the word vector representation of words with context. The attention mechanism is used to learn to attribute different weights of the words to the overall meaning of the text. Experiments over 20 NewsGroup and IMDB datasets demonstrate the effectiveness of our model. Furthermore, we applied our model to hotel review data analysis, which allows us to get more coherent topics from these reviews and achieve good performance in sentiment classification.

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
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