Fast mining and forecasting of complex time-stamped events

Matsubara, Yasuko; Sakurai, Yasushi; Faloutsos, Christos; Iwata, Tomoharu; Yoshikawa, Masatoshi

doi:10.1145/2339530.2339577

Cited by 92 publications

(75 citation statements)

References 25 publications

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“…Machine Learning based approaches have also been used in the literature for temporal modeling tasks in online communities (Matsubara et al, 2012;Danescu-Niculescu-Mizil et al, 2013;Cheng et al, 2015). Recently deep neural networks have shown significant progress due their capability of modeling complex sequential patterns (Ahmed et al, 2010;Lipton et al, 2015;Kuremoto et al, 2014;Qiu et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

“…Models of time-varying user preferences in the recommendation domain (Matsubara et al, 2012;Koren, 2009) generally assume that users evolve according to a 'global clock', whereas patients participating in health forums progress according to his or her own personal timeline. By observing the word usage patterns of users in the site over time, we find that there exist different classes of users.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach

Halder¹,

Poddar²,

Kan³

2017

Proceedings of the 8th Workshop on Computational Approaches To Subjectivity, Sentiment and Social Media Analysis

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Patients turn to Online Health Communities not only for information on specific conditions but also for emotional support. Previous research has indicated that the progression of emotional status can be studied through the linguistic patterns of an individual's posts. We analyze a realworld dataset from the Mental Health section of healthboards.com. Estimated from the word usages in their posts, we find that the emotional progress across patients vary widely.We study the problem of predicting a patient's emotional status in the future from her past posts and we propose a Recurrent Neural Network (RNN) based architecture to address it. We find that the future emotional status can be predicted with reasonable accuracy given her historical posts and participation features. Our evaluation results demonstrate the efficacy of our proposed architecture, by outperforming state-of-the-art approaches with over 0.13 reduction in Mean Absolute Error.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach

Halder¹,

Poddar²,

Kan³

2017

Proceedings of the 8th Workshop on Computational Approaches To Subjectivity, Sentiment and Social Media Analysis

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show abstract

“…An inconvenience is that the prior knowledge is inflexible to adapt to varying requirements. Using the technologies of data mining, it appears that the underlying knowledge of the narratology could be applied to the fields of knowledge summary [40] and extraction [41]. We intend to provide a knowledge learning method that bypasses the assistance of domain experts.…”

Section: Computational Model Of Narrativementioning

confidence: 99%

Data Association at the Level of Narrative Plots to Support Analysis of Spatiotemporal Evolvement of Conflict: A Case Study in Nigeria

Han

Tian

et al. 2016

IJGI

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Abstract:Open data sources regarding conflicts are increasingly enriched by broad social media; these yield a volume of information that exceeds our process capabilities. One of the critical factors is that knowledge extraction from mixed data formats requires systematic, sophisticated modeling. Here, we propose using text mining modeling tools for building associations of heterogeneous semi-structured data to enhance decision-making. Using narrative plots, text representation, and cluster analysis, we provide a data association framework that can mine spatiotemporal data that occur in similar contexts. The framework contains the following steps: (1) a novel text representation is presented to vectorize the textual semantics by learning both co-word features and word orders in a unified form; (2) text clustering technology is employed to associate events of interest with similar events in historical logs, based solely on narrative plots of the events; and (3) the inferred activity procedure is visualized via an evolving spatiotemporal map through the Kriging algorithm. Our results demonstrate that the approach enables deeper discrimination into the trends underlying conflicts and possesses a narrative reasoning forward prediction with a precision of 0.4817, in addition to a high consistency with the conclusions of existing studies.

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“…In related work [6,3] also propose various solutions for answering region retrieval queries, predicting the past and future positions. Similarity search and pattern discovery in time sequences have also attracted huge interest [13,18,15].…”

Section: Related Workmentioning

confidence: 99%

F-Trail: Finding Patterns in Taxi Trajectories

Matsubara

Papalexakis

et al. 2013

Advances in Knowledge Discovery and Data Mining

Self Cite

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Abstract. Given a large number of taxi trajectories, we would like to find interesting and unexpected patterns from the data. How can we summarize the major trends, and how can we spot anomalies? The analysis of trajectories has been an issue of considerable interest with many applications such as tracking trails of migrating animals and predicting the path of hurricanes. Several recent works propose methods on clustering and indexing trajectories data. However, these approaches are not especially well suited to pattern discovery with respect to the dynamics of social and economic behavior. To further analyze a huge collection of taxi trajectories, we develop a novel method, called F-Trail, which allows us to find meaningful patterns and anomalies. Our approach has the following advantages: (a) it is fast, and scales linearly on the input size, (b) it is effective, leading to novel discoveries, and surprising outliers. We demonstrate the effectiveness of our approach, by performing experiments on real taxi trajectories. In fact, F-Trail does produce concise, informative and interesting patterns.

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Fast mining and forecasting of complex time-stamped events

Cited by 92 publications

References 25 publications

Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach

Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach

Data Association at the Level of Narrative Plots to Support Analysis of Spatiotemporal Evolvement of Conflict: A Case Study in Nigeria

F-Trail: Finding Patterns in Taxi Trajectories

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