scholarly journals Encoding Text Information with Graph Convolutional Networks for Personality Recognition

2020 ◽  
Vol 10 (12) ◽  
pp. 4081
Author(s):  
Zhe Wang ◽  
Chun-Hua Wu ◽  
Qing-Biao Li ◽  
Bo Yan ◽  
Kang-Feng Zheng
Keyword(s):  
Personality Traits ◽  
Social Engineering ◽  
Big Five Personality ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Wide Range ◽  
Benchmark Datasets ◽  
Text Information ◽  
Class Labels ◽  
Personality Recognition

Personality recognition is a classic and important problem in social engineering. Due to the small number and particularity of personality recognition databases, only limited research has explored convolutional neural networks for this task. In this paper, we explore the use of graph convolutional network techniques for inferring a user’s personality traits from their Facebook status updates or essay information. Since the basic five personality traits (such as openness) and their aspects (such as status information) are related to a wide range of text features, this work takes the Big Five personality model as the core of the study. We construct a single user personality graph for the corpus based on user-document relations, document-word relations, and word co-occurrence and then learn the personality graph convolutional networks (personality GCN) for the user. The parameters or the inputs of our personality GCN are initialized with a one-hot representation for users, words and documents; then, under the supervision of users and documents with known class labels, it jointly learns the embeddings for users, words, and documents. We used feature information sharing to incorporate the correlation between the five personality traits into personality recognition to perfect the personality GCN. Our experimental results on two public and authoritative benchmark datasets show that the general personality GCN without any external word embeddings or knowledge is superior to the state-of-the-art methods for personality recognition. The personality GCN method is efficient on small datasets, and the average F1-score and accuracy of personality recognition are improved by up to approximately 3.6% and 2.4–2.57%, respectively.

Innovative Teachers, Who Are You? Innovative Teacher Analysis in the Light of The Model оf the ‘Big Five’ Personality Traits

2021 ◽  
Author(s):  
Rina Cohen
Keyword(s):  
Academic Success ◽  
Personality Traits ◽  
Big Five ◽  
Quantitative Methods ◽  
Big Five Personality Traits ◽  
Big Five Personality ◽  
Pedagogical Innovation ◽  
Qualitative And Quantitative ◽  
Qualitative And Quantitative Methods ◽  
Wide Range

In the 21st century, reality, characterized by volatility, uncertainty, complexity and ambiguity, together termed VUCA, change constantly occurs throughout social, technological, economic, environmental, educational, and political (STEEEP model) aspects of society. Therefore, education systems need to adopt innovative approaches to adapt to the frequently changing world. In this study, educational and pedagogical innovation is regarded as including whatever constitutes a change in all areas to which education relates. As teachers are one of the most crucial factors in influencing students’ academic success, and as they must rapidly adapt and constantly innovate to adequately prepare their students for ever-changing circumstances, it is essential to identify traits of innovative teachers. The main goal of this study is to characterize the personality traits of innovative teachers according to the Big Five Personality Traits model, referred to as the NEO-AC model, using qualitative and quantitative methods. The findings show that innovative teachers perceive themselves as first and foremost open to experiences. They are curious people with highly developed imaginations and a wide range of interests. Innovative teachers also may be unconventional, capable of putting together plans and projects from several different disciplines.

scholarly journals Graph convolutional networks: analysis, improvements and results

2021 ◽  
Author(s):  
Ihsan Ullah ◽  
Mario Manzo ◽  
Mitul Shah ◽  
Michael G. Madden
Keyword(s):  
Machine Learning Algorithms ◽  
Complex Structures ◽  
Convolutional Network ◽  
Topological Information ◽  
Computational Costs ◽  
Convolutional Networks ◽  
Complex Organization ◽  
Benchmark Datasets ◽  
Structural Redesign ◽  
Networks Analysis

AbstractA graph can represent a complex organization of data in which dependencies exist between multiple entities or activities. Such complex structures create challenges for machine learning algorithms, particularly when combined with the high dimensionality of data in current applications. Graph convolutional networks were introduced to adopt concepts from deep convolutional networks (i.e. the convolutional operations/layers) that have shown good results. In this context, we propose two major enhancements to two of the existing graph convolutional network frameworks: (1) topological information enrichment through clustering coefficients; and (2) structural redesign of the network through the addition of dense layers. Furthermore, we propose minor enhancements using convex combinations of activation functions and hyper-parameter optimization. We present extensive results on four state-of-art benchmark datasets. We show that our approach achieves competitive results for three of the datasets and state-of-the-art results for the fourth dataset while having lower computational costs compared to competing methods.

Spatial Relational Attention Using Fully Convolutional Networks for Image Caption Generation

2020 ◽  
Vol 19 (02) ◽  
pp. 2050011
Author(s):  
Teng Jiang ◽  
Liang Gong ◽  
Yupu Yang
Keyword(s):  
Spatial Relations ◽  
Image Features ◽  
Attention Mechanism ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Benchmark Datasets ◽  
Visual Concepts ◽  
Image Caption Generation ◽  
Image Caption

Attention-based encoder–decoder framework has greatly improved image caption generation tasks. The attention mechanism plays a transitional role by transforming static image features into sequential captions. To generate reasonable captions, it is of great significance to detect spatial characteristics of images. In this paper, we propose a spatial relational attention approach to consider spatial positions and attributes. Image features are firstly weighted by the attention mechanism. Then they are concatenated with contextual features to form a spatial–visual tensor. The tensor is feature extracted by a fully convolutional network to produce visual concepts for the decoder network. The fully convolutional layers maintain spatial topology of images. Experiments conducted on the three benchmark datasets, namely Flickr8k, Flickr30k and MSCOCO, demonstrate the effectiveness of our proposed approach. Captions generated by the spatial relational attention method precisely capture spatial relations of objects.

Lifestyles in Amazon: Evidence from online reviews enhanced recommender system

2019 ◽  
Vol 62 (6) ◽  
pp. 689-706 ◽  
Author(s):  
Yinghui Huang ◽  
Hui Liu ◽  
Weiqing Li ◽  
Zichao Wang ◽  
Xiangen Hu ◽  
...  
Keyword(s):  
Personality Traits ◽  
Recommender System ◽  
Consumer Preference ◽  
Addition Product ◽  
Online Reviews ◽  
Data Driven ◽  
Big Five Personality ◽  
Predicting Performance ◽  
Wide Range ◽  
Data Driven Approach

Online lifestyles have been shown to reflect and affect consumers’ preferences across a wide range of online scenarios. In the context of e-commerce, it still remains unclear whether online lifestyles are practically influential in predicting consumers’ purchasing preferences across different product categories, especially considering its potential influence over the widely used personality traits. In this study, we provide the first, to the best of our knowledge, quantitative demonstration of online lifestyles in predicting consumers’ online purchasing preferences in e-commerce by using a data-driven approach. We first construct an online lifestyles lexicon including seven distinct dimensions using text mining approaches based on consumers’ language use behaviors. We then incorporate the lexicon in a typical e-commerce recommender system to predict consumers’ purchasing preferences. Experimental results on Amazon Review Dataset show that online lifestyles and all its subdimensions significantly improve preference predicting performance and outperform the widely used Big Five personality traits as a whole. In addition, product types significantly moderate the influence of online lifestyle on consumer preference. The strong empirical evidence indicates that the big e-commerce consumer data facilitates more specialized market psychographic segmentation, which advances data-driven marketing decision-making.

scholarly journals Image-Based Iron Slag Segmentation via Graph Convolutional Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wang Long ◽  
Zheng Junfeng ◽  
Yu Hong ◽  
Ding Meng ◽  
Li Jiangyun
Keyword(s):  
Deep Learning ◽  
Contextual Information ◽  
Recognition Algorithm ◽  
Smart Manufacturing ◽  
Convolutional Network ◽  
Slag Removal ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Iron Slag ◽  
Wide Range

Slagging-off (i.e., slag removal) is an important preprocessing operation of steel-making to improve the purity of iron. Current manual-operated slag removal schemes are inefficient and labor-intensive. Automatic slagging-off is desirable but challenging as the reliable recognition of iron and slag is difficult. This work focuses on realizing an efficient and accurate recognition algorithm of iron and slag, which is conducive to realize automatic slagging-off operation. Motivated by the recent success of deep learning techniques in smart manufacturing, we introduce deep learning methods to this field for the first time. The monotonous gray value of industry images, poor image quality, and nonrigid feature of iron and slag challenge the existing fully convolutional networks (FCNs). To this end, we propose a novel spatial and feature graph convolutional network (SFGCN) module. SFGCN module can be easily inserted in FCNs to improve the reasoning ability of global contextual information, which is helpful to enhance the segmentation accuracy of small objects and isolated areas. To verify the validity of the SFGCN module, we create an industrial dataset and conduct extensive experiments. Finally, the results show that our SFGCN module brings a consistent performance boost for a wide range of FCNs. Moreover, by adopting a lightweight network as backbone, our method achieves real-time iron and slag segmentation. In the future work, we will dedicate our efforts to the weakly supervised learning for quick annotation of big data stream to improve the generalization ability of current models.

A gating context-aware text classification model with BERT and graph convolutional networks

2021 ◽  
pp. 1-13
Author(s):  
Weiqi Gao ◽  
Hao Huang
Keyword(s):  
Text Classification ◽  
Structural Data ◽  
Classification Model ◽  
Context Aware ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Gating Mechanism ◽  
Document Frequency ◽  
Proposed Model ◽  
Benchmark Datasets

Graph convolutional networks (GCNs), which are capable of effectively processing graph-structural data, have been successfully applied in text classification task. Existing studies on GCN based text classification model largely concerns with the utilization of word co-occurrence and Term Frequency-Inverse Document Frequency (TF–IDF) information for graph construction, which to some extent ignore the context information of the texts. To solve this problem, we propose a gating context-aware text classification model with Bidirectional Encoder Representations from Transformers (BERT) and graph convolutional network, named as Gating Context GCN (GC-GCN). More specifically, we integrates the graph embedding with BERT embedding by using a GCN with gating mechanism enables the acquisition of context coding. We carry out text classification experiments to show the effectiveness of the proposed model. Experimental results shown our model has respectively obtained 0.19%, 0.57%, 1.05% and 1.17% improvements over the Text-GCN baseline on the 20NG, R8, R52, and Ohsumed benchmark datasets. Furthermore, to overcome the problem that word co-occurrence and TF–IDF are not suitable for graph construction for short texts, Euclidean distance is used to combine with word co-occurrence and TF–IDF information. We obtain an improvement by 1.38% on the MR dataset compared to Text-GCN baseline.

scholarly journals XLNet-Caps: Personality Classification from Textual Posts

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1360
Author(s):  
Ying Wang ◽  
Jiazhuang Zheng ◽  
Qing Li ◽  
Chenglong Wang ◽  
Hanyun Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Social Networking Sites ◽  
Text Messages ◽  
Big Five Personality ◽  
Long Distance ◽  
Average Prediction Error ◽  
Text Information ◽  
Personality Recognition ◽  
Personality Features ◽  
Personality Classification

Personality characteristics represent the behavioral characteristics of a class of people. Social networking sites have a multitude of users, and the text messages generated by them convey a person’s feelings, thoughts, and emotions at a particular time. These social texts indeed record the long-term psychological activities of users, which can be used for research on personality recognition. However, most of the existing deep learning models for multi-label text classification consider long-distance semantics or sequential semantics, but problems such as non-continuous semantics are rarely studied. This paper proposed a deep learning framework that combined XLNet and the capsule network for personality classification (XLNet-Caps) from textual posts. Our personality classification was based on the Big Five personality theory and used the text information generated by the same user at different times. First, we used the XLNet model to extract the emotional features from the text information at each time point, and then, the extracted features were passed through the capsule network to extract the personality features further. Experimental results showed that our model can effectively classify personality and achieve the lowest average prediction error.

scholarly journals Context Modulated Dynamic Networks for Actor and Action Video Segmentation with Language Queries

2020 ◽  
Vol 34 (07) ◽  
pp. 12152-12159
Author(s):  
Hao Wang ◽  
Cheng Deng ◽  
Fan Ma ◽  
Yi Yang
Keyword(s):  
Video Segmentation ◽  
State Of The Art ◽  
Dynamic Networks ◽  
Specific Region ◽  
Visual Features ◽  
Convolutional Network ◽  
Fine Grained ◽  
Convolutional Networks ◽  
Benchmark Datasets ◽  
Context Features

Actor and action video segmentation with language queries aims to segment out the expression referred objects in the video. This process requires comprehensive language reasoning and fine-grained video understanding. Previous methods mainly leverage dynamic convolutional networks to match visual and semantic representations. However, the dynamic convolution neglects spatial context when processing each region in the frame and is thus challenging to segment similar objects in the complex scenarios. To address such limitation, we construct a context modulated dynamic convolutional network. Specifically, we propose a context modulated dynamic convolutional operation in the proposed framework. The kernels for the specific region are generated from both language sentences and surrounding context features. Moreover, we devise a temporal encoder to incorporate motions into the visual features to further match the query descriptions. Extensive experiments on two benchmark datasets, Actor-Action Dataset Sentences (A2D Sentences) and J-HMDB Sentences, demonstrate that our proposed approach notably outperforms state-of-the-art methods.

scholarly journals Attention-Enhanced Graph Convolutional Networks for Aspect-Based Sentiment Classification with Multi-Head Attention

2021 ◽  
Vol 11 (8) ◽  
pp. 3640
Author(s):  
Guangtao Xu ◽  
Peiyu Liu ◽  
Zhenfang Zhu ◽  
Jie Liu ◽  
Fuyong Xu
Keyword(s):  
Sentiment Classification ◽  
Experimental Results ◽  
Sentence Structure ◽  
Convolutional Network ◽  
Structure Information ◽  
Convolutional Networks ◽  
Dependency Tree ◽  
Syntactic Information ◽  
Benchmark Datasets ◽  
Reasonable Use

The purpose of aspect-based sentiment classification is to identify the sentiment polarity of each aspect in a sentence. Recently, due to the introduction of Graph Convolutional Networks (GCN), more and more studies have used sentence structure information to establish the connection between aspects and opinion words. However, the accuracy of these methods is limited by noise information and dependency tree parsing performance. To solve this problem, we proposed an attention-enhanced graph convolutional network (AEGCN) for aspect-based sentiment classification with multi-head attention (MHA). Our proposed method can better combine semantic and syntactic information by introducing MHA and GCN. We also added an attention mechanism to GCN to enhance its performance. In order to verify the effectiveness of our proposed method, we conducted a lot of experiments on five benchmark datasets. The experimental results show that our proposed method can make more reasonable use of semantic and syntactic information, and further improve the performance of GCN.

scholarly journals Part-Level Graph Convolutional Network for Skeleton-Based Action Recognition

2020 ◽  
Vol 34 (07) ◽  
pp. 11045-11052
Author(s):  
Linjiang Huang ◽  
Yan Huang ◽  
Wanli Ouyang ◽  
Liang Wang
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
The State ◽  
Body Parts ◽  
Convolutional Network ◽  
Joint Level ◽  
Convolutional Networks ◽  
Level Information ◽  
Benchmark Datasets ◽  
High Level

Recently, graph convolutional networks have achieved remarkable performance for skeleton-based action recognition. In this work, we identify a problem posed by the GCNs for skeleton-based action recognition, namely part-level action modeling. To address this problem, a novel Part-Level Graph Convolutional Network (PL-GCN) is proposed to capture part-level information of skeletons. Different from previous methods, the partition of body parts is learnable rather than manually defined. We propose two part-level blocks, namely Part Relation block (PR block) and Part Attention block (PA block), which are achieved by two differentiable operations, namely graph pooling operation and graph unpooling operation. The PR block aims at learning high-level relations between body parts while the PA block aims at highlighting the important body parts in the action. Integrating the original GCN with the two blocks, the PL-GCN can learn both part-level and joint-level information of the action. Extensive experiments on two benchmark datasets show the state-of-the-art performance on skeleton-based action recognition and demonstrate the effectiveness of the proposed method.

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