Learning to classify short and sparse text &amp; web with hidden topics from large-scale data collections

Phan, Xuan-Hieu; Nguyen, Le-Minh; Horiguchi, Susumu

doi:10.1145/1367497.1367510

Cited by 648 publications

(407 citation statements)

References 29 publications

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“…Unlike traditional long texts, short texts are of great difficulty in traditional text mining (such as search, subject discovery, semantic, and emotional analysis) due to their short length, low context information and statistical information. Related studies include the use of external data sources (such as Wikipedia [3], search results [4], etc.) to expand the document, or use the internal similar document information to expand the expression of short text [5].…”

Section: Challenges Posed By Network Big Datamentioning

confidence: 99%

Present and Future of Network Big Data

Xin¹

2017

Proceedings of the 7th International Conference on Education, Management, Information and Computer Science (ICEMC 2017)

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Abstract. Network big data refer to the massive data generated by interaction and fusion of the Ternary human-machine-thing universe in the Cyberspace and available on the Internet. The Increase of their scale and complexity provided that of the capacity of hardware characterized by the Moore law, which brings the grand challenges to the architecture and the processing and computing Capacity of the contemporary IT systems, meanwhile set presents live opportunities on Deep mining and taking full advantage of the big value of network big data. Therefore, it is Pressing to research the disciplinary issues and discover the common laws of network big data, And further study the fundamental theory and basic approach to qualitatively or quantitatively Dealing

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Section: Challenges Posed By Network Big Datamentioning

confidence: 99%

Present and Future of Network Big Data

Xin¹

2017

Proceedings of the 7th International Conference on Education, Management, Information and Computer Science (ICEMC 2017)

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“…[20]. Phan et al employed a similarity estimation method of short text based on the probability distribution of document topic [21]. Zhang and Zhong collected large-scale external data to build the topic model according to the LDA, which enables word topics to enrich feature representations of short text [22].…”

Section: Related Workmentioning

confidence: 99%

A Novel Mobile Video Community Discovery Scheme Using Ontology-Based Semantical Interest Capture

Zhang

Xiong

2016

Mobile Information Systems

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Leveraging network virtualization technologies, the community-based video systems rely on the measurement of common interests to define and steady relationship between community members, which promotes video sharing performance and improves scalability community structure. In this paper, we propose a novel mobile Video Community discovery scheme using ontologybased semantical interest capture (VCOSI). An ontology-based semantical extension approach is proposed, which describes video content and measures video similarity according to video key word selection methods. In order to reduce the calculation load of video similarity, VCOSI designs a prefix-filtering-based estimation algorithm to decrease energy consumption of mobile nodes. VCOSI further proposes a member relationship estimate method to construct scalable and resilient node communities, which promotes video sharing capacity of video systems with the flexible and economic community maintenance. Extensive tests show how VCOSI obtains better performance results in comparison with other state-of-the-art solutions.

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“…However, it focuses meme-tracking [22]. More generally, news processing is an active related domain of research [1,15].…”

Section: Related Workmentioning

confidence: 99%

Raimond: Quantitative Data Extraction from Twitter to Describe Events

Sellam

Alonso

2015

Engineering the Web in the Big Data Era

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Abstract. Social media play a decisive role in communicating and spreading information during global events. In particular, real-time microblogging platforms such as Twitter have become prevalent. Researchers have used microblogging for a number of tasks, including past events analysis, predictions, and information retrieval. Nevertheless, little attention has been given to quantitative data extraction. In this paper, we address two questions: can we develop a mechanism to extract quantitative data from a collection of tweets, and can we use the salient findings to describe an event? To answer the first question, we introduce Raimond, a virtual text curator, specialized in quantitative data extraction from Twitter. To address the second question, we use our system on three events and evaluate its output using a crowdsourcing strategy. We demonstrate the effectiveness of our approach with a number of real world examples.

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Learning to classify short and sparse text & web with hidden topics from large-scale data collections

Cited by 648 publications

References 29 publications

Present and Future of Network Big Data

Present and Future of Network Big Data

A Novel Mobile Video Community Discovery Scheme Using Ontology-Based Semantical Interest Capture

Raimond: Quantitative Data Extraction from Twitter to Describe Events

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