A Survey of Text Mining in Social Media: Facebook and Twitter Perspectives

Salloum, Said A.; Al-Emran, Mostafa; Monem, Azza Abdel; Shaalan, Khaled

doi:10.25046/aj020115

Cited by 176 publications

(70 citation statements)

References 57 publications

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“…In the case of posting comments on any post on social networks, there is no single structured technique in the direct analysis of the data. Data available in the text form has a great importance, and text mining has recently become quite prominent, especially for SNA 63 . Text mining is a multidisciplinary field, involving text analysis, information extraction, information retrieval, computational linguistics, categorization, clustering, visualization, database technology, and machine learning, and also, it can be viewed as an extension of data mining 64,65 .…”

Section: Fundamentals Of Snamentioning

confidence: 99%

Semantic analysis on social networks: A survey

Bayrakdar

Yücedağ

Şimşek

et al. 2020

Int J Communication

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Summary As social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state‐of‐the‐art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.

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Section: Fundamentals Of Snamentioning

confidence: 99%

Semantic analysis on social networks: A survey

Bayrakdar

Yücedağ

Şimşek

et al. 2020

Int J Communication

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“…The first selection step is based on adding or choosing other people to a Facebook contacts, pages of companies, citizens' initiatives, celebrities, political parties, etc., like to subscribe to their news [16]. The basis for these suggestions is the rich data that Facebook has about existing network of relationships as well as preferences and activities.…”

Section: A Understanding Facebook's Selection Stepsmentioning

confidence: 99%

Social Media as an Opinion Formulator: A Study on Implications and Recent Developments

Jameel

Ali

Malik

2019

2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)

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Social media has a great influence on how information reaches us and how we form an opinion based on it. For many users, social media increases the variety of information and ensure its accessibility across different platforms. However, recent years have seen an exponential increase in the power of social media. Most often, the forwarding and sharing of journalistic articles serve the recommendation from the user's point of view, but recently, these platforms have been developed into an echo chamber to transport additional information or even a critical attitude. In addition, the uncontrolled influx of social media in different parts of the world favors phenomena such as fake news and social bots. These threaten the exchange of views sought by mutual understanding and sincerity that democratic societies need. Motivated by these observations, this article aims to paint a realistic picture of the impact of social media. To do so, we discuss the recent developments and social changes and provide analysis of the global impact of the connected audience. Next, we shed light on some of the opinion formulation procedures and its key enablers. Finally, we present an explanatory discussion on the Facebook algorithms and provide two case studies in the wake of recent global events.

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“…The classification of texts is a method of searching for data and exploring the data among large data and classifying them into groups for easy reference [1][2][3][4][5]. Internet pages, books [6], magazines, and social media [7][8][9][10][11][12][13][14][15][16][17][18][19][20], have become a rich source of information that needs to be categorized and organized for easy reference [21][22][23][24][25]. There has been a lot of research in this field, but most of it involves the classification of English and Spanish texts and texts in other languages.…”

Section: Introductionmentioning

confidence: 99%

A systematic review of text classification research based on deep learning models in Arabic language

Wahdan

Hantoobi

Salloum

et al. 2020

IJECE

Self Cite

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Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language.

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A Survey of Text Mining in Social Media: Facebook and Twitter Perspectives

Abstract: Text mining has become one of the trendy fields that has been incorporated in several research fields such as computational linguistics, Information

Cited by 176 publications

References 57 publications

Semantic analysis on social networks: A survey

Semantic analysis on social networks: A survey

Social Media as an Opinion Formulator: A Study on Implications and Recent Developments

A systematic review of text classification research based on deep learning models in Arabic language

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