Construct a Bipartite Signed Network in YouTube

Big Data ◽  
2016 ◽  
pp. 370-391 ◽  
Author(s):  
Tianyuan Yu ◽  
Liang Bai ◽  
Jinlin Guo ◽  
Zheng Yang
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
The State ◽  
Experimental Results ◽  
Online Video ◽  
Video Sharing ◽  
Youtube Videos ◽  
Signed Network ◽  
Analysis Of Social Networks ◽  
State Of Art

Nowadays, the video-sharing websites are becoming more and more popular, which leads to latent social networks among videos and users. In this work, results are integrated with the data collected from YouTube, one of the largest user-driven online video repositories, and are supported by Chinese sentiment analysis which excels the state of art. Along with it, the authors construct two types of bipartite signed networks, video network (VN) and topic participant network (TPN), where nodes denote videos or users while weights of edges represent the correlation between the nodes. Several indices are defined to quantitatively evaluate the importance of the nodes in the networks. Experiments are conducted by using YouTube videos and corresponding metadata related to two specific events. Experimental results show that both the analysis of social networks and indices correspond very closely with the events' evolution and the roles that topic participants play in spreading Internet videos. Finally, the authors extend the networks to summarization of a video set related to an event.

Construct a Bipartite Signed Network in YouTube

2015 ◽  
Vol 6 (4) ◽  
pp. 56-77
Author(s):  
Tianyuan Yu ◽  
Liang Bai ◽  
Jinlin Guo ◽  
Zheng Yang
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
The State ◽  
Experimental Results ◽  
Online Video ◽  
Video Sharing ◽  
Youtube Videos ◽  
Signed Network ◽  
Analysis Of Social Networks ◽  
State Of Art

Nowadays, the video-sharing websites are becoming more and more popular, which leads to latent social networks among videos and users. In this work, results are integrated with the data collected from YouTube, one of the largest user-driven online video repositories, and are supported by Chinese sentiment analysis which excels the state of art. Along with it, the authors construct two types of bipartite signed networks, video network (VN) and topic participant network (TPN), where nodes denote videos or users while weights of edges represent the correlation between the nodes. Several indices are defined to quantitatively evaluate the importance of the nodes in the networks. Experiments are conducted by using YouTube videos and corresponding metadata related to two specific events. Experimental results show that both the analysis of social networks and indices correspond very closely with the events' evolution and the roles that topic participants play in spreading Internet videos. Finally, the authors extend the networks to summarization of a video set related to an event.

scholarly journals A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis

2021 ◽  
Vol 11 (23) ◽  
pp. 11344
Author(s):  
Wei Ke ◽  
Ka-Hou Chan
Keyword(s):  
Probability Distribution ◽  
Information Extraction ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Final Analysis ◽  
The State ◽  
Experimental Results ◽  
Content Adaptive

Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and capture useful features in the hidden state, which represent the content of the entire paragraph. In particular, we introduce the Chebyshev pooling to connect to the end of the CNN-based extractor instead of using the maximum pooling. This can project the features into a probability distribution so as to provide an interpretable evaluation for the final analysis. Experimental results demonstrate the superiority of the proposed approach, being compared to the state-of-the-art models.

scholarly journals YouTube as a resource for evaluating the unmet needs of caregivers of stroke survivors

2019 ◽  
Vol 26 (3) ◽  
pp. 1599-1616 ◽  
Author(s):  
Alexandra MJ Denham ◽  
Amanda L Baker ◽  
Neil J Spratt ◽  
Olivia Wynne ◽  
Sally A Hunt ◽  
...  
Keyword(s):  
Unmet Needs ◽  
Online Communication ◽  
The United States ◽  
Stroke Survivors ◽  
Online Video ◽  
Inclusion Criteria ◽  
Video Sharing ◽  
Youtube Videos ◽  
Insight Into

Content produced by caregivers of stroke survivors on the online video-sharing platform YouTube may be a good source of knowledge regarding caregivers’ unmet needs. We aimed to examine the content, quantity and quality of YouTube videos that target and discuss the needs and concerns of caregivers of stroke survivors. YouTube was systematically searched using six search strings, and the first 20 videos retrieved from each search were screened against the inclusion criteria. A pre-determined coding schedule was used to report the rate of unmet needs in each video. Twenty-six videos were included in the analysis. In total, 291 unmet needs were reported by caregivers of stroke survivors, an average of 11.2 unmet needs per video. The most common unmet needs domain was ‘Impact of Caregiving on Daily Activities’ (44%). Most videos were developed in the United States (61.5%) and featured spouses of stroke survivors (65.47%). Content produced by caregivers of stroke survivors on YouTube may be used as a tool for caregivers to provide and receive support through online communication. YouTube videos offer insight into the unmet needs of caregivers of stroke survivors and may be used as an additional resource for stroke services to support caregivers.

scholarly journals Methodology for sentiment analysis in twitter posts about mobile learning

2021 ◽  
Vol 2 (2) ◽  
pp. 2718-2728
Author(s):  
Hernán Gil-Ramírez ◽  
Rosa María Guilleumas-García
Keyword(s):  
Social Networks ◽  
Communication Network ◽  
Sentiment Analysis ◽  
Mobile Learning ◽  
Research Work ◽  
Complex Interactions ◽  
The Individual ◽  
Negative Feelings ◽  
Analysis Of Social Networks ◽  
The Way

Analysis of social networks has become of great interest to researchers from different areas, including educators, due to Twitter’s growing importance as a space for discussion and dissemination of knowledge and opinions. This reality demands the development of analysis processes that allow to know the topics of interest in the network, the positive or negative feelings in relation to those topics and who the network influencers are. Those objectives guided this research work and in order to achieve them, we developed a methodological proposal for sentiment analysis of tweets. This article describes the process followed, which involved 1) detecting the structure of the communication network, 2) calculating the general metrics, 3) representing the communication network, 4) identifying and analyzing the clusters, 5) calculating their metrics as well as those of the individual nodes and 6) establishing the polarity of the posts published in the network. This paper also describes the methodoly followed to identify trends and topics of interest in the hashtags and web domains included in the tweets. The proposal for analysis presented here is intended to help researchers interested in the field of social networks, to understand the complex interactions that take place in these environments and the way in which information is disseminated, valued and converted into topics of interest thanks to the network users’ actions.

scholarly journals Target-Aspect-Sentiment Joint Detection for Aspect-Based Sentiment Analysis

2020 ◽  
Vol 34 (05) ◽  
pp. 9122-9129
Author(s):  
Hai Wan ◽  
Yufei Yang ◽  
Jianfeng Du ◽  
Yanan Liu ◽  
Kunxun Qi ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
High Performance ◽  
State Of The Art ◽  
Language Model ◽  
The State ◽  
Experimental Results ◽  
Joint Detection ◽  
Sentiment Detection ◽  
Novel Method ◽  
The Given

Aspect-based sentiment analysis (ABSA) aims to detect the targets (which are composed by continuous words), aspects and sentiment polarities in text. Published datasets from SemEval-2015 and SemEval-2016 reveal that a sentiment polarity depends on both the target and the aspect. However, most of the existing methods consider predicting sentiment polarities from either targets or aspects but not from both, thus they easily make wrong predictions on sentiment polarities. In particular, where the target is implicit, i.e., it does not appear in the given text, the methods predicting sentiment polarities from targets do not work. To tackle these limitations in ABSA, this paper proposes a novel method for target-aspect-sentiment joint detection. It relies on a pre-trained language model and can capture the dependence on both targets and aspects for sentiment prediction. Experimental results on the SemEval-2015 and SemEval-2016 restaurant datasets show that the proposed method achieves a high performance in detecting target-aspect-sentiment triples even for the implicit target cases; moreover, it even outperforms the state-of-the-art methods for those subtasks of target-aspect-sentiment detection that they are competent to.

scholarly journals A prototype analysis of machine learning methodologies for sentiment analysis of social networks

2018 ◽  
Vol 7 (2.7) ◽  
pp. 963
Author(s):  
Vijay Kumar Atmakur ◽  
Dr P.Siva Kumar
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Support Vector ◽  
Learning Approaches ◽  
Social Network Data ◽  
Networking Technologies ◽  
Reference Classes ◽  
Analysis Of Social Networks

In present day’s social networking technologies are increased because of different user’s communication with each others. There are different types of networks are available in present situations like face book, twitter and LinkedIn. These are the valuable resources for data mining applications because of prevalence presents of different user’s information present in outside environment. Sentiment analysis is the process that defines attitudes, views, emotions and opinions from text, database sources and tweets. Sentiment analysis involves to categorize data based on different opinions like positive and negative or neutral reference classes. In this paper, we analyze different machine learning approaches to define sentiment analysis on social networks. This paper describes comparative analysis of existing machine learning approaches to classify text and other reference classes to evaluate different metric representations. And also this paper describes different machine learning methodologies like Naïve Bayesian, Entropy max and support vector machine (SVM) research on social network data streams. And also discuss major innovations to evaluate different procedures and challenges of analysis of sentiment or opinion mining aspects in present social networks.  

Evaluation of Stress States of Components Using Ultrasonic and Micro Magnetic Techniques

2009 ◽  
Author(s):  
E. Schneider
Keyword(s):  
The State ◽  
Experimental Results ◽  
Evaluation Procedures ◽  
Magnetic Techniques ◽  
Turbine Rotors ◽  
Stress States ◽  
State Of Art

The paper describes the measuring and evaluation procedures of both techniques. The state of art is characterized by discussing experimental results of applications on e.g. turbine rotors, gear parts and welded plates and sheets. Comparisons with the results of established techniques show the reliability of the ultrasonic and micromagnetic techniques. The applicability as well as the limitations of the techniques will be commented in detail.

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