Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

Abkenar, Sepideh Bazzaz; Kashani, Mostafa Haghi; Mahdipour, Ebrahim; Jameii, Seyed Mahdi

doi:10.1016/j.tele.2020.101517

Cited by 123 publications

(55 citation statements)

References 138 publications

(128 reference statements)

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“…In contrast to a non-structured review process, a systematic review reduces bias and follows a rigorous and precise sequence of methodological stages to research literature. Systematic reviews rely on evaluated and accurate review methods to extract, analyze, and document results [ 13 ]. The first step is to search relevant papers in this domain.…”

Section: Methodsmentioning

confidence: 99%

Towards effective offloading mechanisms in fog computing

Sofla

Kashani

Mahdipour

et al. 2021

Multimed Tools Appl

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Fog computing is considered a formidable next-generation complement to cloud computing. Nowadays, in light of the dramatic rise in the number of IoT devices, several problems have been raised in cloud architectures. By introducing fog computing as a mediate layer between the user devices and the cloud, one can extend cloud computing's processing and storage capability. Offloading can be utilized as a mechanism that transfers computations, data, and energy consumption from the resource-limited user devices to resource-rich fog/cloud layers to achieve an optimal experience in the quality of applications and improve the system performance. This paper provides a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works. Each selected paper's pros and cons are explored and analyzed to state and address the present potentialities and issues of offloading mechanisms in a fog environment efficiently. We classify offloading mechanisms in a fog system into four groups, including computation-based, energy-based, storage-based, and hybrid approaches. Furthermore, this paper explores offloading metrics, applied algorithms, and evaluation methods related to the chosen offloading mechanisms in fog systems. Additionally, the open challenges and future trends derived from the reviewed studies are discussed.

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Section: Methodsmentioning

confidence: 99%

Towards effective offloading mechanisms in fog computing

Sofla

Kashani

Mahdipour

et al. 2021

Multimed Tools Appl

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“…A massive amount of unstructured text data is being constructed by mobility tools, societal media, nets or webs, and the like. For example, in Twitter social media, the voluntary data‐sharing structure provides scholars with data potentially beneficial for their researches 128‐130 . Eruptive information growth is a challenging issue to discover meaningful knowledge via big data analysis.…”

Section: Open Issues and Future Trendsmentioning

confidence: 99%

Leveraging big data in smart cities: A systematic review

Karimi

Kashani

Akbari

et al. 2021

Concurrency and Computation

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Recently, the notion of a smart city, which includes smart well‐being, smart transit, and smart society, has attracted much attention due to its impact on people's quality of living. Data in smart cities are characterized by variety, velocity, volume, value, and veracity that are the well‐known characteristics of big data. The fast pace expanding of IoT devices and sensors in smart cities generates a huge volume of data that can help decision‐makers and managers in city management. The aim of this article is to wholly and systematically review big data handling approaches in smart cities, in which we analyze research efforts published between 2013 and February 2021, where these techniques are categorized based on their algorithms and architectures. Further, the main ideas, evaluation techniques, tools, evaluation metrics, algorithm types, advantages, and disadvantages are explored. Additionally, essential evaluation factors are introduced in which scalability and availability by 16%, time by 15% and accuracy by 11% are more in focus, and finally, some of the challenges, open issues, and future trends that are valuable for further research are suggested in big data handling approaches in smart cities.

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“…With the massive use of social networks such as Facebook, Twitter and Instagram, and dedicated platforms for sharing reviews and comments such as IMDB and Airbnb; it has become extremely difficult to track down published information, let alone extract relevant information such as reviews about a product or service, on the one hand, because of the abundance and variety of published data [1], and on the other hand because of the unstructured nature of the published texts, which makes it almost impossible to analyze them by classical computer methods [2].…”

Section: Introductionmentioning

confidence: 99%

LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model

Chiny¹,

Chihab²,

Bencharef³

et al. 2021

IJACSA

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Most sentiment analysis models that use supervised learning algorithms consume a lot of labeled data in the training phase in order to give satisfactory results. This is usually expensive and leads to high labor costs in real-world applications. This work consists in proposing a hybrid sentiment analysis model based on a Long Short-Term Memory network, a rulebased sentiment analysis lexicon and the Term Frequency-Inverse Document Frequency weighting method. These three (input) models are combined in a binary classification model. In the latter, each of these algorithms has been implemented: Logistic Regression, k-Nearest Neighbors, Random Forest, Support Vector Machine and Naive Bayes. Then, the model has been trained on a limited amount of data from the IMDB dataset. The results of the evaluation on the IMDB data show a significant improvement in the Accuracy and F1 score compared to the best scores recorded by the three input models separately. On the other hand, the proposed model was able to transfer the knowledge gained on the IMDB dataset to better handle a new data from Twitter US Airlines Sentiments dataset.

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Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

Cited by 123 publications

References 138 publications

Towards effective offloading mechanisms in fog computing

Towards effective offloading mechanisms in fog computing

Leveraging big data in smart cities: A systematic review

LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model

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