A Multi-Classification Sentiment Analysis Model of Chinese Short Text Based on Gated Linear Units and Attention Mechanism

2021 ◽  
Vol 20 (6) ◽  
pp. 1-13
Author(s):  
Lei Liu ◽  
Hao Chen ◽  
Yinghong Sun
Keyword(s):  
Sentiment Analysis ◽  
Local Features ◽  
Attention Mechanism ◽  
Semantic Features ◽  
Analysis Model ◽  
Short Text ◽  
Sentiment Lexicon ◽  
Benchmark Datasets ◽  
Convolution Kernels ◽  
Multi Classification

Sentiment analysis of social media texts has become a research hotspot in information processing. Sentiment analysis methods based on the combination of machine learning and sentiment lexicon need to select features. Selected emotional features are often subjective, which can easily lead to overfitted models and poor generalization ability. Sentiment analysis models based on deep learning can automatically extract effective text emotional features, which will greatly improve the accuracy of text sentiment analysis. However, due to the lack of a multi-classification emotional corpus, it cannot accurately express the emotional polarity. Therefore, we propose a multi-classification sentiment analysis model, GLU-RCNN, based on Gated Linear Units and attention mechanism. Our model uses the Gated Linear Units based attention mechanism to integrate the local features extracted by CNN with the semantic features extracted by the LSTM. The local features of short text are extracted and concatenated by using multi-size convolution kernels. At the classification layer, the emotional features extracted by CNN and LSTM are respectively concatenated to express the emotional features of the text. The detailed evaluation on two benchmark datasets shows that the proposed model outperforms state-of-the-art approaches.

scholarly journals A Multichannel Model for Microbial Key Event Extraction Based on Feature Fusion and Attention Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Peng Li ◽  
Qian Wang
Keyword(s):  
Sentiment Analysis ◽  
Feature Fusion ◽  
Attention Mechanism ◽  
Event Extraction ◽  
Semantic Features ◽  
Analysis Model ◽  
Global Features ◽  
Public Health Emergencies ◽  
Multimodal Information ◽  
Local And Global Features

In order to further mine the deep semantic information of the microbial text of public health emergencies, this paper proposes a multichannel microbial sentiment analysis model MCMF-A. Firstly, we use word2vec and fastText to generate word vectors in the feature vector embedding layer and fuse them with lexical and location feature vectors; secondly, we build a multichannel layer based on CNN and BiLSTM to extract local and global features of the microbial text; then we build an attention mechanism layer to extract the important semantic features of the microbial text; thirdly, we merge the multichannel output in the fusion layer and use soft; finally, the results are merged in the fusion layer, and a surtax function is used in the output layer for sentiment classification. The results show that the F1 value of the MCMF-A sentiment analysis model reaches 90.21%, which is 9.71% and 9.14% higher than the benchmark CNN and BiLSTM models, respectively. The constructed dataset is small in size, and the multimodal information such as images and speech has not been considered.

scholarly journals Effect of Supervised and Unsupervised Algorithm for Cross Domain Sentiment Analysis

2020 ◽  
Vol 9 (8) ◽  
pp. 719-723
Keyword(s):  
Sentiment Analysis ◽  
New Technologies ◽  
Information Age ◽  
New Economy ◽  
Support Vector ◽  
Analysis Model ◽  
Cultural Shift ◽  
Cross Domain ◽  
Benchmark Datasets ◽  
Unsupervised Approach

Today we are living in the "information age" where data is the capital of the new economy. With the rapidly growing data every day on online portals and social networking websites, today industries are collecting and analyzing more data than before. Though data is readily available but finding valuable insights out of it is a real task. With easy accessibility of the data, new technologies, and a cultural shift towards data-driven decision making drives the need for Sentiment Analysis (SA) and makes it relevant in most of the domains like politics, marketing, healthcare, etc. This rapidly increasing information on different domains has motivated researchers to develop a cross-domain sentiment analysis model. For the development of this model, we have analyzed the performance of supervised and unsupervised models on benchmark datasets for the cross-domain analysis. The models chosen for the supervised is the Support Vector Machine (SVM) and for the unsupervised approach we have used a combination of Vader wherein the testing results showed that the supervised algorithms performed well in comparison to the unsupervised algorithm.

scholarly journals A Span-based Joint Model for Opinion Target Extraction and Target Sentiment Classification

2019 ◽  
Author(s):  
Yan Zhou ◽  
Longtao Huang ◽  
Tao Guo ◽  
Jizhong Han ◽  
Songlin Hu
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Joint Model ◽  
The State ◽  
Attention Mechanism ◽  
Sentiment Classification ◽  
Global Information ◽  
Target Extraction ◽  
Benchmark Datasets ◽  
Tagging Methods

Target-Based Sentiment Analysis aims at extracting opinion targets and classifying the sentiment polarities expressed on each target. Recently, token based sequence tagging methods have been successfully applied to jointly solve the two tasks, which aims to predict a tag for each token. Since they do not treat a target containing several words as a whole, it might be difficult to make use of the global information to identify that opinion target, leading to incorrect extraction. Independently predicting the sentiment for each token may also lead to sentiment inconsistency for different words in an opinion target. In this paper, inspired by span-based methods in NLP, we propose a simple and effective joint model to conduct extraction and classification at span level rather than token level. Our model first emulates spans with one or more tokens and learns their representation based on the tokens inside. And then, a span-aware attention mechanism is designed to compute the sentiment information towards each span. Extensive experiments on three benchmark datasets show that our model consistently outperforms the state-of-the-art methods.

scholarly journals Sentiment Lexicon-Based Features for Sentiment Analysis in Short Text

2015 ◽  
Vol 90 (1) ◽  
pp. 217-226 ◽  
Author(s):  
Hussam Hamdan ◽  
Patrice Bellot ◽  
Frederic Bechet
Keyword(s):  
Sentiment Analysis ◽  
Short Text ◽  
Sentiment Lexicon

Lexical attention and aspect-oriented graph convolutional networks for aspect-based sentiment analysis

2021 ◽  
pp. 1-12
Author(s):  
Wenwen Li ◽  
Shiqun Yin ◽  
Ting Pu
Keyword(s):  
Sentiment Analysis ◽  
Oriented Graph ◽  
Attention Mechanism ◽  
Experimental Results ◽  
The Other ◽  
Convolutional Networks ◽  
Other Hand ◽  
Syntactic Information ◽  
Benchmark Datasets ◽  
Syntactic Dependencies

 The purpose of aspect-based sentiment analysis is to predict the sentiment polarity of different aspects in a text. In previous work, while attention has been paid to the use of Graph Convolutional Networks (GCN) to encode syntactic dependencies in order to exploit syntactic information, previous models have tended to confuse opinion words from different aspects due to the complexity of language and the diversity of aspects. On the other hand, the effect of word lexicality on aspects’ sentiment polarity judgments has not been considered in previous studies. In this paper, we propose lexical attention and aspect-oriented GCN to solve the above problems. First, we construct an aspect-oriented dependency-parsed tree by analyzing and pruning the dependency-parsed tree of the sentence, then use the lexical attention mechanism to focus on the features of the lexical properties that play a key role in determining the sentiment polarity, and finally extract the aspect-oriented lexical weighted features by a GCN.Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.

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