Multimodal social intelligence in a real-time dashboard system

Gruhl, Daniel; Nagarajan, Meena; Pieper, Jan; Robson, Christine; Sheth, Amit P.

doi:10.1007/s00778-010-0207-5

Cited by 22 publications

(7 citation statements)

References 24 publications

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“…Приклади використання методів ML розглянуто в [5][6][7]. Методи ML часто кладуть в основу побудови таких засобів візуалізації результатів аналізу даних як Dashboard [8][9]. У ньому дані згруповані за змістом для більш легкого візуального сприйняття інформації про бізнес-процеси за значною кількістю показників [10][11][12].…”

Section: вступunclassified

Дослідження ефективності застосування технології Machine Learning Services в задачах прогнозування

Fedko¹

2021

zhnups

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Викладено і проаналізовано результати експериметів щодо ефективності розв’язання задач прогнозування методами Machine Learning із застосуванням технології Machine Learning Services. Ця технологія полягає у перенесенні процесів оброблення даних з комп’ютера клієнта (як це реалізовано у класичній технології Machine Learning) на сервер, на якому зберігаються дані. Дослідження проводилися шляхом порівняння витрат часу розв’язання задач за кожною технологією при різних обсягах даних. Результати досліджень показали, що застосування технології Machine Learning Services має у два рази кращі показники на кількості даних понад півтора мільйона записів.

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Section: вступunclassified

Дослідження ефективності застосування технології Machine Learning Services в задачах прогнозування

Fedko¹

2021

zhnups

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“…9 It also helps in transferring a lot of information that includes headway of communication technology, a bounty of advanced mobile phones and the quick ascent of online networking by means of web clients as video, as opposed to text. 10,11 Also, the sentiment analysis supports in better feature extraction as well as representation of visual content and text content. 12 This is an incredible open door for huge organizations to gain, by separating client sentiment, proposals, and objections on their items from these video surveys.…”

Section: Background and Motivationmentioning

confidence: 99%

“…It is linked with a broad number of utilizations that includes election result, motion picture film industry execution forecast, and forecast financial exchange execution expectation 9 . It also helps in transferring a lot of information that includes headway of communication technology, a bounty of advanced mobile phones and the quick ascent of online networking by means of web clients as video, as opposed to text 10,11 . Also, the sentiment analysis supports in better feature extraction as well as representation of visual content and text content 12 .…”

Section: Introductionmentioning

confidence: 99%

Hybrid grass bee optimization‐multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

Alqahtani

Pandiaraj²,

Murali³

et al. 2021

Concurrency and Computation

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Presently, large recordings like audio and video are uploaded each day on the public networks particularly Facebook, YouTube, etc. Such postings generate unlimited data via the Internet. Over the next few decades adapting and dealing with data mining to obtain relevant details from social media is considered as a difficult task. To tackle these challenges, a novel text-audio-video consistency-driven multimodal sentiment analysis method is proposed in this paper. This proposed system examines the correlation among the text, audio and video followed by multimodal sentiment analysis.A different set of features are extracted and then the extracted features are chosen optimally by employing a new hybrid grass bee optimization algorithm (HGBEE) thus obtaining a feature set containing an optimal value for better precision and low computational time. Then an utterance level multimodular fusion regarding text, audio, and video features is developed. Finally, the proposed multikernal extreme learning classifier (MKELM) is employed for sentiment classification. Then the proposed system is evaluated by testing with three multimodal datasets in terms of precision, accuracy, recall, and F-measure, respectively. From the simulation, it is clear that the proposed system accomplishes the maximum classification accuracy value of 98.3% with minimum computation time. The implementation of our proposed approach is done under the MATLAB platform. K E Y W O R D Saccuracy and computation time, feature level fusion, grass bee, kernal extreme learning classifier, multimodal sentiment analysis Recently, the public network facilitates gathering information for posting and sharing various types of multimodal features in social media without knowing much detail regarding the network topology and architecture. 1 Individuals are now widely utilizing the internet-based life condition, for example, YouTube, Facebook, Blog, and Microblog so as to express their opinions. 2 Here, sentiment analysis acts as a key for unlocking huge data since

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“…A big data dashboard for urban mobility for city operational and planning purposes and as a learning system [86]; a widget-based dashboard to support scholars' awareness of their research networks [79]; a real-time dashboard system with multimodal social intelligence [87].…”

Section: Social Analyticalmentioning

confidence: 99%

Geospatial Dashboards for Monitoring Smart City Performance

Jing

et al. 2019

Sustainability

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Geospatial dashboards have attracted increasing attention from both user communities and academic researchers since the late 1990s. Dashboards can gather, visualize, analyze and advise on urban performance to support sustainable development of smart cities. We conducted a critical review of the research and development of geospatial dashboards, including the integration of maps, spatial data analytics, and geographic visualization for decision support and real-time monitoring of smart city performance. The research about this kind of system has mainly focused on indicators, information models including statistical models and geospatial models, and other related issues. This paper presents an overview of dashboard history and key technologies and applications in smart cities, and summarizes major research progress and representative developments by analyzing their key technical issues. Based on the review, we discuss the visualization model and validity of models for decision support and real-time monitoring that need to be further researched, and recommend some future research directions.

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Multimodal social intelligence in a real-time dashboard system

Cited by 22 publications

References 24 publications

Дослідження ефективності застосування технології Machine Learning Services в задачах прогнозування

Дослідження ефективності застосування технології Machine Learning Services в задачах прогнозування

Hybrid grass bee optimization‐multikernal extreme learning classifier: Multimodular fusion strategy and optimal feature selection for multimodal sentiment analysis in social media videos

Geospatial Dashboards for Monitoring Smart City Performance

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