Analyzing Social Network Data Using Deep Neural Networks: A Case Study Using Twitter Posts

Liao, Wen‐Hung; Huang, Yen-Ting; Yang, Tsu-Hsuan; Wu, Yi-Chieh

doi:10.1109/ism46123.2019.00053

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Tweets from Taiwan during 2017 are analyzed to categorize into travel and nontravel classes. The authors integrated hierarchical clustering with deep neural network [51].…”

Section: Related Workmentioning

confidence: 99%

A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis

et al. 2020

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Twitter sentiment analysis is a challenging problem in natural language processing. For this purpose, supervised learning techniques have mostly been employed, which require labeled data for training. However, it is very time consuming to label datasets of large size. To address this issue, unsupervised learning techniques such as clustering can be used. In this study, we explore the possibility of using hierarchical clustering for twitter sentiment analysis. Three hierarchical-clustering techniques, namely single linkage (SL), complete linkage (CL) and average linkage (AL), are examined. A cooperative framework of SL, CL and AL is built to select the optimal cluster for tweets wherein the notion of optimal-cluster selection is operationalized using majority voting. The hierarchical clustering techniques are also compared with k-means and two state-of-the-art classifiers (SVM and Naïve Bayes). The performance of clustering and classification is measured in terms of accuracy and time efficiency. The experimental results indicate that cooperative clustering based on majority voting approach is robust in terms of good quality clusters with tradeoff of poor time efficiency. The results also suggest that the accuracy of the proposed clustering framework is comparable to classifiers which is encouraging. INDEX TERMS Cooperative clustering, majority voting, sentiment analysis, twitter sentiment analysis.

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“…Tweets from Taiwan during 2017 are analyzed to categorize into travel and nontravel classes. The authors integrated hierarchical clustering with deep neural network [51].…”

Section: Related Workmentioning

confidence: 99%

A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis

et al. 2020

View full text Add to dashboard Cite

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“…The aim in these applications is to predict a variable of direct interest to social scientists, usually on the assumption that the available data are especially suited for this methodology. In addition, there have been many adjacent uses of ANNs with bearing on the social sciences, such as enriching datasets with demographic information based on facial recognition (Mancosu and Bobba 2019), classifying messages on Twitter (Gambäck and Sikdar 2017;Liao et al 2019), and recognising collective actions in image sequences (Bagautdinov et al 2017). Such applications of neural networks can form part of a larger investigation employing more traditional types of models.…”

Section: The Use Of Neural Network In the Social Sciencesmentioning

confidence: 99%

Ontology, neural networks, and the social sciences

Strohmaier

2020

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The ontology of social objects and facts remains a field of continued controversy. This situation complicates the life of social scientists who seek to make predictive models of social phenomena. For the purposes of modelling a social phenomenon, we would like to avoid having to make any controversial ontological commitments. The overwhelming majority of models in the social sciences, including statistical models, are built upon ontological assumptions that can be questioned. Recently, however, artificial neural networks (ANNs) have made their way into the social sciences, raising the question whether they can avoid controversial ontological assumptions. ANNs are largely distinguished from other statistical and machine learning techniques by being a representation-learning technique. That is, researchers can let the neural networks select which features of the data to use for internal representation instead of imposing their preconceptions. On this basis, I argue that neural networks can avoid ontological assumptions to a greater degree than common statistical models in the social sciences. I then go on, however, to establish that ANNs are not ontologically innocent either. The use of ANNs in the social sciences introduces ontological assumptions typically in at least two ways, via the input and via the architecture.

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Social network analysis using deep learning: applications and schemes

Abbas

2021

Soc. Netw. Anal. Min.

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Analyzing Social Network Data Using Deep Neural Networks: A Case Study Using Twitter Posts

Cited by 4 publications

References 2 publications

A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis

A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis

Ontology, neural networks, and the social sciences

Social network analysis using deep learning: applications and schemes

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