Tracking Time Evolution of Collective Attention Clusters in Twitter: Time Evolving Nonnegative Matrix Factorisation

Saito, Sakuyoshi; Hirata, Yoshito; Sasahara, Kazutoshi; Suzuki, Hideyuki

doi:10.1371/journal.pone.0139085

Cited by 8 publications

(6 citation statements)

References 69 publications

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“…We also developed two matrix decomposition methods-Rolling-ONMF and Sliding-ONMF-for identifying the hidden topics and their temporal evolution from the collected tweets. Compared to existing temporal topic models [20,21], our proposed models could deal with the large scale of tweets and infer the hidden topics in a reasonable time. Two models have different strengths.…”

Section: Resultsmentioning

confidence: 99%

“…To model the temporal topic patterns through NMF, Time Evolving Non-negative Matrix Factorization (TENMF) [21] was introduced. Given a tweet collection, all tweets will be separated into a set of sequential bins by their timestamps.…”

Section: Detection Of Topic Evolution In Social Mediamentioning

confidence: 99%

“…3, we use the last topic dictionary of t − 1, W t−1,Q , as prior knowledge to guide the ONMF to learn topics from t. In this way, we only need to change the (5) into (8) at the adjacent batches between two time slices. The idea behind (8) is inspired by TENMF [21], which aims at learning topics from previous time to current time. With this objective, the topic i at time t is assumed as an evolution from the topic i in time t − 1.…”

Section: Rolling-onmfmentioning

confidence: 99%

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What Are People Concerned About During the Pandemic? Detecting Evolving Topics about COVID-19 from Twitter

Chang

Monselise

Yang

2021

J Healthc Inform Res

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With the novel coronavirus (COVID-19) pandemic affecting the lives of the citizens of over 200 countries, there is a need for policy makers and clinicians to understand public sentiment and track the spread of the disease. One of the sources for gaining valuable insight into public sentiment is through social media. This study aims to extract this insight by producing a list of the most discussed topics regarding COVID-19 on Twitter every week and monitoring the evolution of topics from week to week. This research will propose two topic mining that can handle a large-scale datasetrolling online non-negative matrix factorization (Rolling-ONMF) and sliding online non-negative matrix factorization (Sliding-ONMF)-and compare the insights produced by both techniques. Each algorithm produces 425 topics over the course of 17 weeks. However, topics that have not evolved from one week to the next beyond a certain evolution threshold are consolidated into a single topic. Since the topics produced by the Rolling-ONMF algorithm each week depend on the topics from the previous week, we find that the Sliding-ONMF algorithm produces more varied topics each week; however, the topics produced by the Rolling-ONMF algorithm contain keywords that appear more consistent with each other when reviewing the terms manually. We also observe that the Sliding-ONMF algorithm is able to capture events that have shorter time frames rather than ones that last throughout many months while the Rolling-ONMF algorithm detects more general themes due to a higher average evolution score which leads to more topic consolidation. We have also conducted a qualitative analysis and grouped the detected topics into themes. A number of important themes such as government policy, economic crisis, COVID-19-related updates,

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Section: Resultsmentioning

confidence: 99%

Section: Detection Of Topic Evolution In Social Mediamentioning

confidence: 99%

Section: Rolling-onmfmentioning

confidence: 99%

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What Are People Concerned About During the Pandemic? Detecting Evolving Topics about COVID-19 from Twitter

Chang

Monselise

Yang

2021

J Healthc Inform Res

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“…In the Twitter data analysis scenario, NMF has been used to analyze Twitter networks so as to capture trends (Kim et al , 2013; Pei et al , 2015), to learn topics from correlation data of terms derived from short texts (Yan et al , 2018), for emotion detection from text written in Indonesian language (Arifin et al , 2014) or to unveil political opinions (Mankad and Michailidis, 2015). Several works have been also proposed to modeling the evolution of topics so as to aid a fast discovery of emerging themes in streaming social media content (Saha and Sindhwani, 2012; Lai et al , 2016; Panisson et al , 2014; Saito et al , 2015; Atsuho, 2017; Shin et al , 2017).…”

Section: Related Workmentioning

confidence: 99%

A framework for intelligent Twitter data analysis with non-negative matrix factorization

Casalino

Castiello

Buono

et al. 2018

IJWIS

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Purpose The purpose of this paper is to propose a framework for intelligent analysis of Twitter data. The purpose of the framework is to allow users to explore a collection of tweets by extracting topics with semantic relevance. In this way, it is possible to detect groups of tweets related to new technologies, events and other topics that are automatically discovered. Design/methodology/approach The framework is based on a three-stage process. The first stage is devoted to dataset creation by transforming a collection of tweets in a dataset according to the vector space model. The second stage, which is the core of the framework, is centered on the use of non-negative matrix factorizations (NMF) for extracting human-interpretable topics from tweets that are eventually clustered. The number of topics can be user-defined or can be discovered automatically by applying subtractive clustering as a preliminary step before factorization. Cluster analysis and word-cloud visualization are used in the last stage to enable intelligent data analysis. Findings The authors applied the framework to a case study of three collections of Italian tweets both with manual and automatic selection of the number of topics. Given the high sparsity of Twitter data, the authors also investigated the influence of different initializations mechanisms for NMF on the factorization results. Numerical comparisons confirm that NMF could be used for clustering as it is comparable to classical clustering techniques such as spherical k-means. Visual inspection of the word-clouds allowed a qualitative assessment of the results that confirmed the expected outcomes. Originality/value The proposed framework enables a collaborative approach between users and computers for an intelligent analysis of Twitter data. Users are faced with interpretable descriptions of tweet clusters, which can be interactively refined with few adjustable parameters. The resulting clusters can be used for intelligent selection of tweets, as well as for further analytics concerning the impact of products, events, etc. in the social network.

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“…The extracted latent features are the underlying unobservable variables that contain most of the useful information of the original data in a much smaller dimension. NMF methods have been successfully used in a variety of applications [27,28], including image processing [26,29], text mining [30], recommender systems [31], and big data analysis [32]. NMF guarantees the final matrices have no negative elements, which is consistent with taxi flows.NMF was applied in spatiotemporal data for analyzing human mobility.…”

mentioning

confidence: 99%

A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips

Gao

Liu

et al. 2019

Sustainability

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Taxi services provide an urban transport option to citizens. Massive taxi trajectories contain rich information for understanding human travel activities, which are essential to sustainable urban mobility and transportation. The origin and destination (O-D) pairs of urban taxi trips can reveal the spatiotemporal patterns of human mobility and then offer fundamental information to interpret and reform formal, functional, and perceptual regions of cities. Matrices are one of the most effective models to represent taxi trajectories and O-D trips. Among matrix representations, non-negative matrix factorization (NMF) gives meaningful interpretations of complex latent relationships. However, the independence assumption for observations is violated by spatial and temporal autocorrelation in taxi flows, which is not compensated in classical NMF models. In order to discover human intra-urban mobility patterns, a novel spatiotemporal constraint NMF (STC-NMF) model that explicitly solves spatial and temporal dependencies is proposed in this paper. It factorizes taxi flow matrices in both spatial and temporal aspects, thus revealing inherent spatiotemporal patterns. With three-month taxi trajectories harvested in Beijing, China, the STC-NMF model is employed to investigate taxi travel patterns and their spatial interaction modes. As the results, four departure patterns, three arrival patterns, and eight spatial interaction patterns during weekdays and weekends are discovered. Moreover, it is found that intensive movements within certain time windows are significantly related to region functionalities and the spatial interaction flows exhibit an obvious distance decay tendency. The outcome of the proposed model is more consistent with the inherent spatiotemporal characteristics of human intra-urban movements. The knowledge gained in this research would be useful to taxi services and transportation management for promoting sustainable urban development.Sustainability 2019, 11, 4214 2 of 22 knowledge [6], including human mobility patterns [7,8], driving strategies of cabdrivers for seeking passengers [9][10][11][12], and urban spatial structures [13][14][15], for understanding human mobility and even improving sustainable urban transport.A taxi trip depends on the specific travel purpose. The origin and destination (O-D) points of a trip are considered the critical elements to represent mobility motions, while the route itself is less semantically representative for passengers. Generally, an O-D pair is exactly the pair of pick-up and drop-off points of a taxi ride, which represents the starting and stopping locations of a complete individual trip trajectory. O-D flows generalize not only trajectory geometries but also mobility semantics. Thus, previous studies have explored them to understand human mobility and urban structures. Direction and distance distributions of taxi trips were investigated to model the geographical heterogeneity and distance decay effect of intra-urban human mobility [8]. Mobility hotspots [16], movemen...

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Tracking Time Evolution of Collective Attention Clusters in Twitter: Time Evolving Nonnegative Matrix Factorisation

Cited by 8 publications

References 69 publications

What Are People Concerned About During the Pandemic? Detecting Evolving Topics about COVID-19 from Twitter

What Are People Concerned About During the Pandemic? Detecting Evolving Topics about COVID-19 from Twitter

A framework for intelligent Twitter data analysis with non-negative matrix factorization

A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips

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