Virtual Reality Technology: Analysis based on text and opinion mining

Palos-Sánchez, Pedro R.; Folgado-Fernández, José Antonio; Sánchez, Mario Alberto Rojas

doi:10.3934/mbe.2022367

Cited by 19 publications

(8 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sánchez et al [ 47 ] analyzed the scope of virtual reality (VR) technology using YouTube data. User sentiments are judged for contribution of VR technology for sustainability of natural environments.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of adjective and adverb types for effective Twitter sentiment classification

Ali,

Masood

2024

PLoS ONE

View full text Add to dashboard Cite

Twitter, the largest microblogging platform, has reported more than 330 million active users in recent years. Many users express their sentiments about politics, sports, products, personalities, etc. Sentiment analysis has emerged as a specialized branch of machine learning in which tweets are binary-classified to provide sentimental insights. A major step in sentiment classification is feature selection, which primarily revolves around parts of speech (POS). Few techniques merely focused on single features such as adjectives, adverbs, and verbs, while other techniques examined types of these features, such as comparative adjectives, superlative adjectives, or general adverbs. Furthermore, POS as linguistic entities have also been studied and extensively classified by researchers, such as CLAWS-C7. For sentiment analysis, none of the studies conceptualized all possible POS features under similar conditions to draw firm conclusion. This research is centered on the following objectives: 1) examining the impact of various types of adjectives and adverbs that have not been previously explored for sentiment classification; 2) analyzing potential combinations of adjectives and adverbs types 3) conducting a comparison with a benchmark dataset for better classification accuracy. To assess the concept, a renowned human annotated dataset of tweets is investigated. Results showed that classification accuracy for adjectives is improved up to 83% based on the general superlative adjective whereas for adverbs, comparative general adverb also depicted significant accuracy improvement. Their combination with general adjectives and general adverbs also played a substantial role. The unexplored potential of adjectives and adverb types proved better in accuracy against state-of-the-art probabilistic model. In comparison to lexicon-based model, proposed research model overruled the dependency of lexicon-based dictionary where each term first needs to be matched for semantic orientation. The evident outcomes also help in time reduction aspect where huge volume of data need to be processed swiftly. This noteworthy contribution brought up significant knowledge and direction for domain experts. In the future, the proposed technique will be explored for other types of textual data across different domains.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of adjective and adverb types for effective Twitter sentiment classification

Ali,

Masood

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…In terms of method selection, Saura, Reyes-Menendez, and Palos-Sanchez illustrate how Twitter sentiment analysis can be effectively used to gauge public reaction to events such as Black Friday sales, a technique that could also be applied to public health crises ( 5 ). Similarly, Palos-Sánchez, Folgado-Fernández, and Rojas-Sánchez applied text and opinion mining techniques in analyzing the discourse surrounding virtual reality technology, demonstrating their usefulness in extracting actionable insights from large volumes of text data ( 6 ). Additionally, Saura, Palos-Sanchez, and Grilo used sentiment analysis through text data mining to identify indicators of startup business success, reinforcing the potential of these analytical methods in various research fields ( 7 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the collaborative evolution process of information in public health emergencies based on complex adaptive system theory and social network analysis: a case study of the COVID-19 pandemic

Lv,

Luo,

Shan

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

IntroductionThis review aimed to elucidate the significance of information collaboration in the prevention and control of public health emergencies, and its evolutionary pathway guided by the theory of complex adaptive systems.MethodsThe study employed time-slicing techniques and social network analysis to translate the dynamic evolution of information collaboration into a stage-based static representation. Data were collected from January to April 2020, focusing on the COVID-19 pandemic. Python was used to amass data from diverse sources including government portals, public commentary, social organizations, market updates, and healthcare institutions. Post data collection, the structures, collaboration objectives, and participating entities within each time slice were explored using social network analysis.ResultsThe findings suggest that the law of evolution for information collaboration in public health emergencies primarily starts with small-scale collaboration, grows to full-scale in the middle phase, and then reverts to small-scale in the final phase. The network’s complexity increases initially and then gradually decreases, mirroring changes in collaboration tasks, objectives, and strategies.DiscussionThe dynamic pattern of information collaboration highlighted in this study offers valuable insights for enhancing emergency management capabilities. Recognizing the evolving nature of information collaboration can significantly improve information processing efficiency during public health crises.

show abstract

“…Recently, VR technology has been increasingly used in movement training to enhance the learning and practice of various movement skills (Lin et al , 2022; Palos Sánchez et al , 2022). VR technology allows users to interact with a simulated environment and experience it as if it were real, and this immersive experience can be used to create realistic and engaging training scenarios (Li and Wong, 2021).…”

Section: Introductionmentioning

confidence: 99%

Immersive innovations: an examination of the efficacy and evolution of virtual reality in human movement training

Deng,

Wang,

Dong

et al. 2023

RIA

View full text Add to dashboard Cite

Purpose This systematic review, following preferred reporting items for systematic reviews and meta-analysis guidelines, rigorously investigates the emergent role of virtual reality (VR) technology in human movement training. The purpose of this study is to explore the effectiveness and evolution of VR in enhancing movement training experiences. Design/methodology/approach Acknowledging its pivotal role in diverse applications, such as sports and rehabilitation, human movement training is currently experiencing accelerated evolution, facilitated by the proliferation of wearable devices and mobile applications. This review conducted an exhaustive search across five different electronic databases, such as Web of Science, PubMed and ProQuest, resulting in the selection of 69 eligible articles published within the past five years. It also integrates 40 studies into a narrative summary, categorized based on the level of immersion offered by respective VR systems. Findings Enhanced immersion in VR potentially augments the effectiveness of movement training by engendering more realistic and captivating experiences for users. The immersive and interactive environments provided by VR technology enable tailored training experiences accompanied by precise, objective feedback. This review highlights the benefits of VR in human movement training and its potential to revolutionize the way training is conducted. Originality/value This systematic review contributes significantly to the existing literature by providing a comprehensive examination of the efficacy and evolution of VR in human movement training. By organizing the findings based on the level of immersion offered by VR systems, it provides valuable insights into the importance of immersion in enhancing training outcomes. In addition, this study identifies the need for future research focusing on the impacts of VR on learning and performance, as well as strategies to optimize its effectiveness and improve accessibility.

show abstract

Virtual Reality Technology: Analysis based on text and opinion mining

Cited by 19 publications

References 75 publications

Evaluation of adjective and adverb types for effective Twitter sentiment classification

Evaluation of adjective and adverb types for effective Twitter sentiment classification

Research on the collaborative evolution process of information in public health emergencies based on complex adaptive system theory and social network analysis: a case study of the COVID-19 pandemic

Immersive innovations: an examination of the efficacy and evolution of virtual reality in human movement training

Contact Info

Product

Resources

About