Feature selection method on twitter dataset with part-of-speech (PoS) pattern applied to traffic analysis

Mounica, B; Lavanya, K.

doi:10.1007/s13198-022-01677-3

Cited by 6 publications

(3 citation statements)

References 41 publications

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“…Data is periodically archived and backed up on cloud servers for scalability ( Quinde et al, 2020 ) and load balancing. Standard processes of machine learning algorithms, such as data cleaning, preprocessing, parameter tuning, feature ( Mounica & Lavanya, 2022 ) and model selection and prediction, are applied in the anxiety prediction model. The anxiety prediction model is continuously trained based on participant assessment responses and social media data, such as tweets and Facebook posts.…”

Section: Case Studymentioning

confidence: 99%

An adaptive data-driven architecture for mental health care applications

Sundaram,

Subramaniam,

Ab Hamid

et al. 2024

PeerJ

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Background In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders. Objective Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated. Method Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale. Results Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture’s effectiveness. To further assess the architecture’s practical application, a prototype architecture for predicting pandemic anxiety was developed.

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Section: Case Studymentioning

confidence: 99%

An adaptive data-driven architecture for mental health care applications

Sundaram,

Subramaniam,

Ab Hamid

et al. 2024

PeerJ

View full text Add to dashboard Cite

show abstract

“…Notwithstanding the extensive research utilizing big data to enhance transfer behavior and optimize travel modalities [24,25], the potential of integrating social media insights with the physical spatial aspects of transit has not been fully explored. Prior studies have harnessed social media data for traffic analyses and predictive modeling, often sidelining the significance of diverse transportation environments.…”

Section: Social Media and Travel Behavior Researchmentioning

confidence: 99%

Exploring Passenger Satisfaction in Multimodal Railway Hubs: A Social Media-Based Analysis of Travel Behavior in China’s Major Rail Stations

Zeng,

Wang,

Gao

et al. 2024

Sustainability

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This study investigates the dynamics of passenger satisfaction and sustainable urban mobility within the context of multimodal railway hubs, focusing on travel behaviors at major stations in China. Against the backdrop of rapid urbanization and the nation’s initiatives to improve transportation efficiency, this research employs social media data analysis to assess passenger sentiment across six key transportation hubs in Eastern China. Utilizing methodological approaches such as keyword frequency analysis and semantic categorization of 39,061 Dianping reviews, supplemented by network visualizations with Gephi, this study reveals insights into factors influencing passenger satisfaction beyond travel efficiency. Signage quality, facility availability, queueing, and crowding emerge as significant determinants of passenger behavior. The study underscores the importance of strategic improvements in station design, navigational aids, and facility management, grounded in real-time data analytics and passenger feedback, to enhance overall passenger satisfaction and promote sustainable urban mobility. This research contributes to advancing understanding of passenger behavior and informs efforts aimed at improving urban transportation systems to meet the evolving needs of passengers and cities.

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“…(i) This section compares the proposed BOW approach with SentWordNet [53] and UMLS [54], VADER [55], and TextBlob lexicon [56], relying on the semantic SA method that suffers from the issue of neglecting a neutral score. Next, two lists of the terms were generated, wherein BOW is the first, and four lexicons are fused as the second list that relied on the hypernym's procedure.…”

Section: Lexicon Generationmentioning

confidence: 99%

Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach

Waheeb

2023

Applied Sciences

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This paper discusses the challenges associated with a class imbalance in medical data and the limitations of current approaches, such as machine multi-task learning (MMTL), in addressing these challenges. The proposed solution involves a novel hybrid data sampling method that combines SMOTE, a meta-weigher with a meta-based self-training method (MMS), and one-sided selection (OSS) to balance the distribution of classes. The method also utilizes condensed nearest neighbors (CNN) to remove noisy majority examples and redundant examples. The proposed technique is twofold, involving the creation of artificial instances using SMOTE-OSS-CNN to oversample the under-represented class distribution and the use of MMS to train an instructor model that produces in-field knowledge for pseudo-labeled examples. The student model uses these pseudo-labels for supervised learning, and the student model and MMS meta-weigher are jointly trained to give each example subtask-specific weights to balance class labels and mitigate the noise effects caused by self-training. The proposed technique is evaluated on a discharge summary dataset against six state-of-the-art approaches, and the results demonstrate that it outperforms these approaches with complete labeled data and achieves results equivalent to state-of-the-art methods that require all labeled data using aspect-based sentiment analysis (ABSA).

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Feature selection method on twitter dataset with part-of-speech (PoS) pattern applied to traffic analysis

Cited by 6 publications

References 41 publications

An adaptive data-driven architecture for mental health care applications

An adaptive data-driven architecture for mental health care applications

Exploring Passenger Satisfaction in Multimodal Railway Hubs: A Social Media-Based Analysis of Travel Behavior in China’s Major Rail Stations

Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach

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