ModalNet: an aspect-level sentiment classification model by exploring multimodal data with fusion discriminant attentional network

2021 ◽  
Author(s):  
Zhe Zhang ◽  
Zhu Wang ◽  
Xiaona Li ◽  
Nannan Liu ◽  
Bin Guo ◽  
...  
Keyword(s):  
Sentiment Classification ◽  
Classification Model ◽  
Multimodal Data ◽  
Attentional Network

scholarly journals SC-BiCapsNet: A Sentiment Classification Model Based on Bi-Channel Capsule Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171801-171813 ◽  
Author(s):  
Kejun Zhang ◽  
Meng Jiao ◽  
Xiaoming Chen ◽  
Zhiqiang Wang ◽  
Biao Liu ◽  
...  
Keyword(s):  
Sentiment Classification ◽  
Classification Model ◽  
Model Based

scholarly journals Lexicon-Enhanced Attention Network Based on Text Representation for Sentiment Classification

2019 ◽  
Vol 9 (18) ◽  
pp. 3717 ◽  
Author(s):  
Wenkuan Li ◽  
Dongyuan Li ◽  
Hongxia Yin ◽  
Lindong Zhang ◽  
Zhenfang Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Attention Mechanism ◽  
Sentiment Classification ◽  
Classification Model ◽  
Linguistic Knowledge ◽  
Great Success ◽  
Text Representation ◽  
Attention Network ◽  
Sentiment Lexicon

Text representation learning is an important but challenging issue for various natural language processing tasks. Recently, deep learning-based representation models have achieved great success for sentiment classification. However, these existing models focus on more semantic information rather than sentiment linguistic knowledge, which provides rich sentiment information and plays a key role in sentiment analysis. In this paper, we propose a lexicon-enhanced attention network (LAN) based on text representation to improve the performance of sentiment classification. Specifically, we first propose a lexicon-enhanced attention mechanism by combining the sentiment lexicon with an attention mechanism to incorporate sentiment linguistic knowledge into deep learning methods. Second, we introduce a multi-head attention mechanism in the deep neural network to interactively capture the contextual information from different representation subspaces at different positions. Furthermore, we stack a LAN model to build a hierarchical sentiment classification model for large-scale text. Extensive experiments are conducted to evaluate the effectiveness of the proposed models on four popular real-world sentiment classification datasets at both the sentence level and the document level. The experimental results demonstrate that our proposed models can achieve comparable or better performance than the state-of-the-art methods.

Research on Multi-Channel Semantic Fusion Classification Model

2019 ◽  
Vol 23 (6) ◽  
pp. 1044-1051
Author(s):  
Di Yang ◽  
◽  
Ningjia Qiu ◽  
Lin Cong ◽  
Huamin Yang
Keyword(s):  
Adaptive Learning ◽  
Sentiment Classification ◽  
Classification Model ◽  
Classification Task ◽  
Model Parameters ◽  
Semantic Features ◽  
Gradient Descent Algorithm ◽  
Text Word ◽  
High Level ◽  
Rate Gradient

In this work, we propose a multi-channel semantic fusion convolutional neural network (SFCNN) to solve the problem of emotional ambiguity caused by the change of contextual order in sentiment classification task. Firstly, the emotional tendency weights are evaluated on the text word vector through the improved emotional tendency attention mechanism. Secondly, the multi-channel semantic fusion layer is leveraged to combine deep semantic fusion of sentences with contextual order to generate deep semantic vectors, which are learned by CNN to extract high-level semantic features. Finally, the improved adaptive learning rate gradient descent algorithm is employed to optimize the model parameters, and completes the sentiment classification task. Three datasets are used to evaluate the effectiveness of the proposed algorithm. The experimental results show that the SFCNN model has the high steady-state precision and generalization performance.

scholarly journals Multimodal Data Evaluation for Classification Problems

2021 ◽  
Author(s):  
Daniela Moctezuma ◽  
Víctor Muníz ◽  
Jorge García
Keyword(s):  
Semantic Representation ◽  
User Profiling ◽  
Classification Model ◽  
Classification Problems ◽  
Multimodal Data ◽  
Lexical Representations ◽  
Pros And Cons ◽  
Main Input ◽  
Text Images ◽  
Media Data

Social media data is currently the main input to a wide variety of research works in many knowledge fields. This kind of data is generally multimodal, i.e., it contains different modalities of information such as text, images, video or audio, mainly. To deal with multimodal data to tackle a specific task could be very difficult. One of the main challenges is to find useful representations of the data, capable of capturing the subtle information that the users who generate that information provided, or even the way they use it. In this paper, we analysed the usage of two modalities of data, images, and text, both in a separate way and by combining them to address two classification problems: meme's classification and user profiling. For images, we use a textual semantic representation by using a pre-trained model of image captioning. Later, a text classifier based on optimal lexical representations was used to build a classification model. Interesting findings were found in the usage of these two modalities of data, and the pros and cons of using them to solve the two classification problems are also discussed.

Negative emotion diffusion and intervention countermeasures of social networks based on deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 4935-4945
Author(s):  
Qiuyun Cheng ◽  
Yun Ke ◽  
Ahmed Abdelmouty
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Negative Emotion ◽  
Sentiment Classification ◽  
Classification Model ◽  
Learning Models ◽  
Data Set ◽  
Position Information ◽  
Processing Technologies ◽  
And Training

Aiming at the limitation of using only word features in traditional deep learning sentiment classification, this paper combines topic features with deep learning models to build a topic-fused deep learning sentiment classification model. The model can fuse topic features to obtain high-quality high-level text features. Experiments show that in binary sentiment classification, the highest classification accuracy of the model can reach more than 90%, which is higher than that of commonly used deep learning models. This paper focuses on the combination of deep neural networks and emerging text processing technologies, and improves and perfects them from two aspects of model architecture and training methods, and designs an efficient deep network sentiment analysis model. A CNN (Convolutional Neural Network) model based on polymorphism is proposed. The model constructs the CNN input matrix by combining the word vector information of the text, the emotion information of the words, and the position information of the words, and adjusts the importance of different feature information in the training process by means of weight control. The multi-objective sample data set is used to verify the effectiveness of the proposed model in the sentiment analysis task of related objects from the classification effect and training performance.

Sign in / Sign up

Export Citation Format

Share Document