Detecting Hot Topics From Academic Big Data

Wang, Beibei; Yang, Bo; Shan, Shuangshuang; Chen, Hechang

doi:10.1109/access.2019.2960285

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…However, as a postprocessing method, although classification can distinguish public opinion content from ordinary content, it cannot improve the recognition of burst content. Wang et al [34] monitor academic keywords based on a deep walk method for achieving dynamic monitoring of cutting-edge topics in different academic fields. Dong et al [35] propose a novel calculation method of a data similarity graph based on single graph clustering, which can simultaneously calculate the data similarity graph on the appropriate scale and detect events on different scales.…”

Section: B Public Opinion Topic Identification Methods Based On Clustmentioning

confidence: 99%

ST-ETM: A Spatial-Temporal Emergency Topic Model for Public Opinion Identifying in Social Networks

2020

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With the development and popularization of social networks, many users and authoritative media broach and share topics through social networks every day. This type of sharing is widespread and is sometimes the first communication of topics of public discussion. Therefore, research on identifying public opinion topics on social networks is of great significance. However, social network posts are short, sparse and noisy, which creates challenges for this research. To overcome these challenges, we propose a spatialtemporal emergency topic model (ST-ETM) to identify public opinion topics in social networks. By introducing spatial-temporal features into the topic model to construct spatial-temporal regions for focusing on topics, the ST-ETM can alleviate the sparsity problem of context in social networks and succeed in focusing on public opinion topics. Moreover, to automatically identify public opinion topics, we introduce the burstiness of the words as a priori in the model, and binary switch variables are combined to automatically identify public opinion topics in social networks. Based on a real Sina Weibo dataset, several comparative experiments are designed to evaluate the performance of our ST-ETM. The experimental results verify the effectiveness of the proposed ST-ETM method. INDEX TERMS public opinion topic, social network, topic model, topic identification.

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Section: B Public Opinion Topic Identification Methods Based On Clustmentioning

confidence: 99%

ST-ETM: A Spatial-Temporal Emergency Topic Model for Public Opinion Identifying in Social Networks

2020

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“…In textual model the aim is to generate keywords directly from the original text [5]. The simplest model in this category uses TF-IDF technique to extract keywords.…”

Section: Related Workmentioning

confidence: 99%

“…A group of one have used lexical and syntactic analyses. Other approaches of this class take the advantages of numerical statistics such as term frequency (TF) or term frequency-inverse document frequency (TF-IDF) [5]. On the other hand, graph-based approaches create a various group of methods by constructing a graph of words.…”

Section: Introductionmentioning

confidence: 99%

Phraseformer: Multimodal Key-phrase Extraction using Transformer and Graph Embedding

Nikzad-Khasmakhi,

Feizi-Derakhshi,

Asgari-Chenaghlu

et al. 2021

Preprint

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Background Keyword extraction is a popular research topic in the field of natural language processing. Keywords are terms that describe the most relevant information in a document. The main problem that researchers are facing is how to efficiently and accurately extract the core keywords from a document. However, previous keyword extraction approaches have utilized the text and graph features, there is the lack of models that can properly learn and combine these features in a best way.Methods In this paper, we develop a multimodal Key-phrase extraction approach, namely Phraseformer, using transformer and graph embedding techniques. In Phraseformer, each keyword candidate is presented by a vector which is the concatenation of the text and structure learning representations. Phraseformer takes the advantages of recent researches such as BERT and ExEm to preserve both representations. Also, the Phraseformer treats the key-phrase extraction task as a sequence labeling problem solved using classification task. ResultsWe analyze the performance of Phraseformer on three datasets including Inspec, SemEval2010 and SemEval 2017 by F1-score. Also, we investigate the performance of different classifiers on Phraseformer method over Inspec dataset. Experimental results demonstrate the effectiveness of Phraseformer method over the three datasets used. Additionally, the Random Forest classifier gain the highest F1-score among all classifiers.Conclusions Due to the fact that the combination of BERT and ExEm is more meaningful and can better represent the semantic of words. Hence, Phraseformer significantly outperforms single-modality methods.

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“…Uses of big data approach in educational domain were witnessed to explore the popular topic of study [12] Inclusion of deep learning concept has assisted in constructing such framework that could further facilitate in extraction of keywords. The work carried out by [13] have developed a model using structural equation modeling.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Framework for Big Data Analytics in Education

Shidaganti¹,

Prakash²

2021

IJACSA

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With the adoption of cloud services for hosting knowledge delivery system in educational domain, there is a surplus quantity of education data being generated every day by current learning management system. Such data are associated with certain typical complexities that impose significant challenges for existing database management and analytics. Review of existing approaches towards educational data highlights that they do not offer full-fledged solution towards analytics and still there is an open-end problem. Therefore, the proposed system introduces a comprehensive framework which offers integrated operation of transformation, data quality, and predictive analytics. The emphasis is more towards achieving distributed analytical operation towards educational data in cloud. Implemented using analytical research methodology, the proposed system shows better analytical performance with respect to frequently used educational data analytical approaches.

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Detecting Hot Topics From Academic Big Data

Cited by 14 publications

References 21 publications

ST-ETM: A Spatial-Temporal Emergency Topic Model for Public Opinion Identifying in Social Networks

ST-ETM: A Spatial-Temporal Emergency Topic Model for Public Opinion Identifying in Social Networks

Phraseformer: Multimodal Key-phrase Extraction using Transformer and Graph Embedding

A Comprehensive Framework for Big Data Analytics in Education

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