Big Data Management and Analytics in Scientific Programming: A Deep Learning-Based Method for Aspect Category Classification of Question-Answering-Style Reviews

Wu, Hanqian; Liu, Mumu; Zhang, Shangbin; Wang, Zhike; Cheng, Siliang

doi:10.1155/2020/4690974

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…The collection process of the crawler system may vary according to the specific application scenarios and functional requirements, but in general, it can be summarized as the following steps. Figure 2 shows the corresponding collection process [13].…”

Section: Methodsmentioning

confidence: 99%

Commodity Information Collection and System Construction of e-Commerce Platform Based on Network Data

2022

Wireless Communications and Mobile Computing

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In order to quickly and accurately collect the massive commodities and corresponding transaction data of large-scale e-commerce platforms, and improve the ability of data analysis and mining, this paper proposes a platform commodity information collection system based on splash technology. The system prerenders the javascript code in the product page, combined with the Scrapy crawler framework, to realize a system that quickly and effectively collects product data from different platforms, and uses “mobile phone” as the retrieval keyword to verify the designed system, respectively. The experimental results show that the system can effectively collect up to 60,000 comments and 6,000 system requests. Conclusion. The platform commodity information collection system based on splash technology has certain application value and promotion for the commodity data collection of different platforms of e-commerce.

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Section: Methodsmentioning

confidence: 99%

Commodity Information Collection and System Construction of e-Commerce Platform Based on Network Data

2022

Wireless Communications and Mobile Computing

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“…Another thrilling revelation is that most studies [70,77,79,84,94,97,106,108,[116][117][118][119][120][121] used Deep Neural Networks (DNN), such as LSTM and Convolutional Neural Network (CNN), as shown in Fig. 6.…”

Section: Big Data Platforms Tool In Bdamentioning

confidence: 99%

“…We observed that several studies [70,83,85,87,88,92,95,97,104,108,117,119,122,124,125] combined two or more metrics to evaluate their models and framework.…”

Section: Evaluation Metrics Used In Bdamentioning

confidence: 99%

“…Even though most Artificial Intelligence (AI) and Machine Learning (ML) algorithms and their enabling platforms for performing BDA are free, they require a new skill set that is uncommon to most practitioners in Big Data Mining and Analytics, June 2022, 5 (2): [81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97] this field and organisations' IT departments [3] . Hence, integrating these tools and platforms seamlessly into an organisation's internal and external data on a common platform is a challenge.…”

Section: Introductionmentioning

confidence: 99%

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A mini-review of machine learning in big data analytics: Applications, challenges, and prospects

Nti

Quarcoo

Aning

et al. 2022

Big Data Min. Anal.

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The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the high number of free BDA tools, platforms, and data mining tools makes it challenging to select the appropriate one for the right task. This paper presents a comprehensive mini-literature review of ML in BDA, using a keyword search; a total of 1512 published articles was identified. The articles were screened to 140 based on the study proposed novel taxonomy. The study outcome shows that deep neural networks (15%), support vector machines (15%), artificial neural networks (14%), decision trees (12%), and ensemble learning techniques (11%) are widely applied in BDA.The related applications fields, challenges, and most importantly the openings for future research, are detailed.

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“…(Our research differs from the work they built on conventional reviews, our proposed model is based on Q&A-style reviews and we leverage conditional random fields to further improve ATE's performance.) More recently, Wu et al [33] proposed a 4-dimension textual representation model on Q&A style reviews for ACC task.…”

Section: Aspect Category Classificationmentioning

confidence: 99%

Multi-task learning based on question–answering style reviews for aspect category classification and aspect term extraction on GPU clusters

Cheng

Wang

et al. 2020

Cluster Comput

Self Cite

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Cluster computing technologies are rapidly advancing and user-generated online reviews are booming in the current Internet and e-commerce environment. The latest question–answering (Q&A)-style reviews are novel, abundant and easily digestible product reviews that also contain massive valuable information for customers. In this paper, we mine valuable aspect information of products contained in these reviews on GPU clusters. To achieve this goal, we utilize two subtasks of aspect-based sentiment analysis: aspect term extraction (ATE) and aspect category classification (ACC). Most previous works focused on only one task or solved these two tasks separately, even though they are highly interrelated, and they do not make full use of abundant training resources. To address this problem, we propose a novel multi-task neural learning model to jointly handle these two tasks and explore the performance of our model on GPU clusters. We conducted extensive comparative experiments on an annotated corpus and found that our proposed model outperforms several baseline models in ATE and ACC tasks on GPU clusters, yielding significant strides in data mining for these types of reviews.

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Big Data Management and Analytics in Scientific Programming: A Deep Learning-Based Method for Aspect Category Classification of Question-Answering-Style Reviews

Cited by 4 publications

References 15 publications

Commodity Information Collection and System Construction of e-Commerce Platform Based on Network Data

Commodity Information Collection and System Construction of e-Commerce Platform Based on Network Data

A mini-review of machine learning in big data analytics: Applications, challenges, and prospects

Multi-task learning based on question–answering style reviews for aspect category classification and aspect term extraction on GPU clusters

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