Spatiotemporal‐based sentiment analysis on tweets for risk assessment of event using deep learning approach

Parimala, M.; Priya, R.; Maddikunta, Praveen Kumar Reddy; Chowdhary, Chiranji Lal; Poluru, Ravi Kumar; Khan, Suleman

doi:10.1002/spe.2851

Cited by 42 publications

(19 citation statements)

References 33 publications

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“…Using DL to forecast other parameters, for instance, hotel room prices, airline ticket prices, etc. (Zhang et al, 2019d;Nie et al, 2020;Liu and Shen, 2020;Verma et al, 2019;Parimala et al, 2021;Ampountolas and Legg, 2021) 15 Provide Provide more tailored experience Using DL to provide a personalised customer experience. For instance, using DL to provide customised advertising or emotion recognition.…”

Section: Forecasting Other Parametermentioning

confidence: 99%

Deep learning in hospitality and tourism: a research framework agenda for future research

Essien

Chukwukelu²

2022

IJCHM

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Purpose This study aims to provide a systematic review of the existing literature on the applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for future research. Design/methodology/approach Covering a five-year time span (2017–2021), this study systematically reviews journal articles archived in four academic databases: Emerald Insight, Springer, Wiley Online Library and ScienceDirect. All 159 articles reviewed were characterised using six attributes: publisher, year of publication, country studied, type of value created, application area and future suggestions (and/or limitations). Findings Five application areas and six challenge areas are identified, which characterise the application of DL in hospitality, tourism and travel. In addition, it is observed that DL is mainly used to develop novel models that are creating business value by forecasting (or projecting) some parameter(s) and promoting better offerings to tourists. Research limitations/implications Although a few prior papers have provided a literature review of artificial intelligence in tourism and hospitality, none have drilled-down to the specific area of DL applications within the context of hospitality, tourism and travel. Originality/value To the best of the authors’ knowledge, this paper represents the first theoretical review of academic research on DL applications in hospitality, tourism and travel. An integrated framework is proposed to expose future research trajectories wherein scholars can contribute significant value. The exploration of the DL literature has significant implications for industry and practice, given that this, as far as the authors know, is the first systematic review of existing literature in this research area.

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Section: Forecasting Other Parametermentioning

confidence: 99%

Deep learning in hospitality and tourism: a research framework agenda for future research

Essien

Chukwukelu²

2022

IJCHM

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“…The developed method was highly desirable as it always maintained the high lists and it did not rank last on any factors. Parimala et al 20 proposed a method called risk assessment and sentiment analysis (RASA) to detect emotions using tweets and emojis, this algorithm analyzes and evaluates the risk associated with any disaster. This model can perform in binary class and multiclass scenario but gave better results in multiclass scenario when compared with all other techniques.…”

Section: Motivationmentioning

confidence: 99%

FSOA‐DNFNet: Incremental indexing and image classification using hybrid optimization‐based deep neuro Fuzzy network

Nadendla

Srikrishna

Rao

2022

Concurrency and Computation

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The rapid evolution and tremendous growth of internet has provided massive growth of unstructured data that leads to a complexity while retrieving dynamic data effectively. The rapid growth in data volume has imposed many challenging constraints, such as necessity to retrieve data completely even if newly arrived samples are occurred and storing of huge volume of data. This has paved a way for concentrating more on incremental learning that functions on information streams. To speed up retrieval, clustering methods and indexes are utilized and periodic updating of clusters is very substantial because of dynamic nature of databases. Moreover, the standard of clustering techniques purely based on data representation techniques, in which traditional methods faced problems like dimensionality explosion and sparsity. To address such limitations, an effectual strategy is developed for incremental indexing and image classification using proposed Feedback Social Optimization Algorithm (FSOA). The image classification is effectively carried out using Deep neuro fuzzy optimizer and it is trained by employing the proposed FSOA and newly FSOA is derived by the integration of Feedback Artificial Tree (FAT) Algorithm and Social Optimization Algorithm (SOA). Moreover, the proposed FSOA has achieved the maximum clustering accuracy of 93.382, the maximum testing accuracy of 94.4, the maximum sensitivity of 91.892, and the maximum specificity of 96.058.

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“…Alla et al [9] did not use a lot of light to beautify the plant landscape by using LED light source but made the plant landscape more beautiful than before. Sybilski et al [10] focused on creating artistic conception of design. Through the use of light, the artistic conception of landscape lighting can be created, and the light and shadow of plant lighting can be handled skillfully to achieve a more poetic atmosphere.…”

Section: Research Status At Home and Abroadmentioning

confidence: 99%

Research on Digital Application of Lighting Design in Public Space Based on Cloud Computing and Data Mining

Huang

Zhang

2021

Wireless Communications and Mobile Computing

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Light comes along with everything in the world, which makes the world full of vigor and vitality. The appearance and development of urban landscape art lighting have added a surprise to human beings and have also increased their desire and pursuit for life. This paper describes the characteristics and functions of intelligent lighting control system in landscape public space based on cloud computing and big data, and how to use remote measurement and control technology. The system is composed of intelligent sensing layer, network transport layer, and big data processing application layer, which can collect the location and running status information of urban lighting equipment in real time. The application mode, management mode, and service mode of traditional lighting were changed through a remote platform, and the sustainable development mode of energy saving and environmental protection in the future was realized. The importance of humanized lighting design in landscape public space and the scientificalness of humanized lighting design in landscape public space lighting design are further clarified. This paper hopes to have certain reference value and guiding significance in landscape public space lighting design.

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Spatiotemporal‐based sentiment analysis on tweets for risk assessment of event using deep learning approach

Cited by 42 publications

References 33 publications

Deep learning in hospitality and tourism: a research framework agenda for future research

Deep learning in hospitality and tourism: a research framework agenda for future research

FSOA‐DNFNet: Incremental indexing and image classification using hybrid optimization‐based deep neuro Fuzzy network

Research on Digital Application of Lighting Design in Public Space Based on Cloud Computing and Data Mining

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