scholarly journals Twitter Sentiment Analysis using Aspect-based Bidirectional Gated Recurrent Unit with Self-Attention Mechanism

2020 ◽  
Vol 13 (5) ◽  
pp. 97-110
Author(s):  
Mohan Venkataramaiah ◽  
◽  
Nandakumar Achar ◽  
Keyword(s):  
Sentiment Analysis ◽  
Attention Mechanism ◽  
Gated Recurrent Unit

scholarly journals Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaodi Wang ◽  
Xiaoliang Chen ◽  
Mingwei Tang ◽  
Tian Yang ◽  
Zhen Wang
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Network Models ◽  
Attention Mechanism ◽  
Sentiment Classification ◽  
Classification Model ◽  
Neural Network Models ◽  
Position Information ◽  
Gated Recurrent Unit ◽  
Relative Position Information

The aim of aspect-level sentiment analysis is to identify the sentiment polarity of a given target term in sentences. Existing neural network models provide a useful account of how to judge the polarity. However, context relative position information for the target terms is adversely ignored under the limitation of training datasets. Considering position features between words into the models can improve the accuracy of sentiment classification. Hence, this study proposes an improved classification model by combining multilevel interactive bidirectional Gated Recurrent Unit (GRU), attention mechanisms, and position features (MI-biGRU). Firstly, the position features of words in a sentence are initialized to enrich word embedding. Secondly, the approach extracts the features of target terms and context by using a well-constructed multilevel interactive bidirectional neural network. Thirdly, an attention mechanism is introduced so that the model can pay greater attention to those words that are important for sentiment analysis. Finally, four classic sentiment classification datasets are used to deal with aspect-level tasks. Experimental results indicate that there is a correlation between the multilevel interactive attention network and the position features. MI-biGRU can obviously improve the performance of classification.

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.

A Speech Enhancement Method Using Attention Mechanism and Gated Recurrent Unit

2021 ◽  
Author(s):  
Kaibei Peng ◽  
Xiaoming Sun ◽  
Haowei Chen ◽  
Zhen He ◽  
Jianrong Wang
Keyword(s):  
Speech Enhancement ◽  
Attention Mechanism ◽  
Enhancement Method ◽  
Gated Recurrent Unit

scholarly journals Emotion-Semantic-Enhanced Bidirectional LSTM with Multi-Head Attention Mechanism for Microblog Sentiment Analysis

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 280
Author(s):  
Shaoxiu Wang ◽  
Yonghua Zhu ◽  
Wenjing Gao ◽  
Meng Cao ◽  
Mengyao Li
Keyword(s):  
Sentiment Analysis ◽  
Short Term Memory ◽  
Syntactic Structure ◽  
Contextual Information ◽  
Research Field ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Mechanism Model ◽  
Hidden Layer ◽  
The Impact

The sentiment analysis of microblog text has always been a challenging research field due to the limited and complex contextual information. However, most of the existing sentiment analysis methods for microblogs focus on classifying the polarity of emotional keywords while ignoring the transition or progressive impact of words in different positions in the Chinese syntactic structure on global sentiment, as well as the utilization of emojis. To this end, we propose the emotion-semantic-enhanced bidirectional long short-term memory (BiLSTM) network with the multi-head attention mechanism model (EBILSTM-MH) for sentiment analysis. This model uses BiLSTM to learn feature representation of input texts, given the word embedding. Subsequently, the attention mechanism is used to assign the attentive weights of each words to the sentiment analysis based on the impact of emojis. The attentive weights can be combined with the output of the hidden layer to obtain the feature representation of posts. Finally, the sentiment polarity of microblog can be obtained through the dense connection layer. The experimental results show the feasibility of our proposed model on microblog sentiment analysis when compared with other baseline models.

scholarly journals DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series

2020 ◽  
Vol 34 (01) ◽  
pp. 930-937
Author(s):  
Qingxiong Tan ◽  
Mang Ye ◽  
Baoyao Yang ◽  
Siqi Liu ◽  
Andy Jinhua Ma ◽  
...  
Keyword(s):  
Time Series ◽  
Missing Values ◽  
Multivariate Time Series ◽  
Medical Knowledge ◽  
Attention Mechanism ◽  
Physiological Variables ◽  
Real World Datasets ◽  
Clinical Records ◽  
Gated Recurrent Unit ◽  
Time Aware

Due to the discrepancy of diseases and symptoms, patients usually visit hospitals irregularly and different physiological variables are examined at each visit, producing large amounts of irregular multivariate time series (IMTS) data with missing values and varying intervals. Existing methods process IMTS into regular data so that standard machine learning models can be employed. However, time intervals are usually determined by the status of patients, while missing values are caused by changes in symptoms. Therefore, we propose a novel end-to-end Dual-Attention Time-Aware Gated Recurrent Unit (DATA-GRU) for IMTS to predict the mortality risk of patients. In particular, DATA-GRU is able to: 1) preserve the informative varying intervals by introducing a time-aware structure to directly adjust the influence of the previous status in coordination with the elapsed time, and 2) tackle missing values by proposing a novel dual-attention structure to jointly consider data-quality and medical-knowledge. A novel unreliability-aware attention mechanism is designed to handle the diversity in the reliability of different data, while a new symptom-aware attention mechanism is proposed to extract medical reasons from original clinical records. Extensive experimental results on two real-world datasets demonstrate that DATA-GRU can significantly outperform state-of-the-art methods and provide meaningful clinical interpretation.

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