Spreading social influence with both positive and negative opinions in online networks

He, Jing; Han, Meng; Ji, Shouling; Du, Tianyu; Li, Zhao

doi:10.26599/bdma.2018.9020034

Cited by 28 publications

(18 citation statements)

References 38 publications

(61 reference statements)

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“…The work of Huynh et al [15] have used big data approach and convolution neural network over opinion mining from social network using Vietnamese language. The work of He et al [16] has used binary mechanism for classifying opinion from online network in order to identify potential node. Classification problems associated with opinion from social network was investigated by Fernandes et al [17].…”

Section: A Backgroundmentioning

confidence: 99%

Framework for opinion as a service on review data of customer using semantics based analytics

Rajeshwari¹,

Vagdevi²

2020

IJECE

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At Opinion mining plays a significant role in representing the original and unbiased perception of the products/services. However, there are various challenges associated with performing an effective opinion mining in the present era of distributed computing system with dynamic behaviour of users. Existing approaches is more laborious towards extracting knowledge from the reviews of user which is further subjected to various rounds of operation with complex procedures. The proposed system addresses the problem by introducing a novel framework called as Opinion-as-a-Service which is meant for direct utilization of the extracted knowledge in most user friendly manner. The proposed system introduces a set of three sequential algorithm that performs aggregated of incoming stream of opinion data, performing indexing, followed by applying semantics for extracting knowledge. The study outcome shows that proposed system is better than existing system in mining performance.

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Section: A Backgroundmentioning

confidence: 99%

Framework for opinion as a service on review data of customer using semantics based analytics

Rajeshwari¹,

Vagdevi²

2020

IJECE

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“…Social media technologies were deployed as the main knowledge sharing mechanisms among US government agencies during 2010 Haitian earthquakes [23,24]. The previous studies design Twitter-based systems to identify natural disasters and to improve situation awareness in critical times [25,26,27,28,8]. Topic modeling, a text-mining tool, is used to discover the abstract topics from a collection of data [19].…”

Section: Related Workmentioning

confidence: 99%

“…With high-volume and real-time data, social media can at times outperform news sources on timeliness when reporting some types of events [8,9,10,11]. For instance, it is quite a hot topic that the Amazon rain forest had alarming clusters of burning wildfires in summer 2019.…”

Section: Introductionmentioning

confidence: 99%

Understanding Social Media Beyond Text: A Reliable Practice on Twitter

Hou

Han

2020

Preprint

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Social media has been broadly applied in many applications in sales, marketing, event detection, etc. With high-volume and real-time data, social media has also been used for disaster responses. However, distinguishing between rumors and reliable information can be challenging, since social media, a user-generated content system, has a great number of users who update massive information every second. Furthermore, the rich information is not only included in the short text content but also embedded in the images, videos. In this paper, to address the emerging challenge of disaster response, we introduce a reliable framework for disaster information understanding and response with a practice on Twitter. The framework integrates both textual and imagery content from tweets in hope to fully utilize the information. The text classifier is built to remove noises, which can achieve 0.92 F1-score in classifying individual tweet. The image classifier is constructed by fine-tuning pre-trained VGG-F network, which can achieve 90\% accuracy. The image classifier serves as a verifier in the pipeline to reject or confirm the detected events. The evaluation indicates that the verifier can significantly reduce false positive events. We also explore Twitter-based drought management system and infrastructure monitoring system to further study the impacts of imagery content on event detection systems and we are able to pinpoint additional benefits which can be gained from social media imagery content.

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“…In recent years, the problem of climate change has caused the attention from all over the world. As one of the most significant factors in water resource ecosystem, precipitation plays an important role in meteorological fields, which has a strong impact on human's daily lives as well as business such as agriculture and construction [1][2][3]. The variations in time and quantity of rainfall have the potential impact on the agriculture yield and disaster management [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

Tian¹,

Lei²,

Liu³

et al. 2020

J Cloud Comp

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Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an exponential relation between radar reflectivity factors and precipitation called Z-R relationship which has a low accuracy in precipitation estimation. With the rapid development of computing power in cloud computing, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of precipitation estimation, given an ample account of radar data. In this study, we introduce these approaches to the precipitation estimation, proposing two models based on the back propagation neural networks (BPNN) and convolutional neural networks (CNN) respectively, to compare with the traditional method in meteorological service systems. The results of the three approaches show that deep learning algorithms outperform the traditional method with 75.84% and 82.30% lower mean square errors respectively. Meanwhile, the proposed method with CNN achieves a better performance than that with BPNN for its ability to preserve the spatial information by maintaining the interconnection between pixels, which improves 26.75% compared to that with BPNN.

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Spreading social influence with both positive and negative opinions in online networks

Cited by 28 publications

References 38 publications

Framework for opinion as a service on review data of customer using semantics based analytics

Framework for opinion as a service on review data of customer using semantics based analytics

Understanding Social Media Beyond Text: A Reliable Practice on Twitter

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

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