Annotating semantic tags of locations in location-based social networks

Li, Yanhui; Zhao, Xibin; Zhang, Zhen; Yuan, Ye; Wang, Guoren

doi:10.1007/s10707-019-00367-w

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…The locations in social networks have also attracted the attention of many researchers. Li et al [44] proposed an approach called the similar user pattern (SUP) for tagging the locations in social networks. The approach considers the similarities of the check-in activities made by the users.…”

Section: Semantic Taggingmentioning

confidence: 99%

A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management

Jussila¹,

Alkhammash²,

Alghamdi³

et al. 2022

Computers, Materials &Amp; Continua

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Social media platforms provide new value for markets and research companies. This article explores the use of social media data to enhance customer value propositions. The case study involves a company that develops wearable Internet of Things (IoT) devices and services for stress management. Netnography and semantic annotation for recognizing and categorizing the context of tweets are conducted to gain a better understanding of users' stress management practices. The aim is to analyze the tweets about stress management practices and to identify the context from the tweets. Thereafter, we map the tweets on pleasure and arousal to elicit customer insights. We analyzed a case study of a marketing strategy on the Twitter platform. Participants in the marketing campaign shared photos and texts about their stress management practices. Machine learning techniques were used to evaluate and estimate the emotions and contexts of the tweets posted by the campaign participants. The computational semantic analysis of the tweets was compared to the text analysis of the tweets. The content analysis of only tweet images resulted in 96% accuracy in detecting tweet context, while that of the textual content of tweets yielded an accuracy of 91%. Semantic tagging by Ontotext was able to detect correct tweet context with an accuracy of 50%.

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Section: Semantic Taggingmentioning

confidence: 99%

A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management

Jussila¹,

Alkhammash²,

Alghamdi³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…e authors in [6] used a probabilistic topic model considering the factors like user interests, temporal and spatial pa ern, rating score to nd category-aware and sentimental tags. A recent work in [9] extracts user similarities and performs multi-label semantic annotation with extreme learning machine.…”

Section: Related Workmentioning

confidence: 99%

“…Now, the posterior distribution is obtained by combining the prior with the likelihood de ned in Equation (9).…”

Section: Expectationmentioning

confidence: 99%

HAP-SAP: Semantic Annotation in LBSNs using Latent Spatio-Temporal Hawkes Process

Dubey,

Srijith,

Desarkar

2020

Preprint

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e prevalence of location-based social networks (LBSNs) has eased the understanding of human mobility pa erns. Knowledge of human dynamics can aid in various ways like urban planning, managing tra c congestion, personalized recommendation etc. ese dynamics are in uenced by factors like social impact, periodicity in mobility, spatial proximity, in uence among users and semantic categories etc., which makes location modelling a critical task. However, categories which act as semantic characterization of the location, might be missing for some check-ins and can adversely a ect modelling the mobility dynamics of users. At the same time, mobility pa erns provide a cue on the missing semantic category. In this paper, we simultaneously address the problem of semantic annotation of locations and location adoption dynamics of users. We propose our model HAP-SAP, a latent spatio-temporal multivariate Hawkes process, which considers latent semantic category in uences, and temporal and spatial mobility pa erns of users. e model parameters and latent semantic categories are inferred using expectation-maximization algorithm, which uses Gibbs sampling to obtain posterior distribution over latent semantic categories. e inferred semantic categories can supplement our model on predicting the next check-in events by users. Our experiments on real datasets demonstrate the e ectiveness of the proposed model for the semantic annotation and location adoption modelling tasks.

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Frequent Semantic Trajectory Sequence Pattern Mining in Location-Based Social Networks

Zhang

et al. 2020

Web and Big Data

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Annotating semantic tags of locations in location-based social networks

Cited by 8 publications

References 40 publications

A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management

A Netnographic-Based Semantic Analysis of Tweet Contents for Stress Management

HAP-SAP: Semantic Annotation in LBSNs using Latent Spatio-Temporal Hawkes Process

Frequent Semantic Trajectory Sequence Pattern Mining in Location-Based Social Networks

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