A Survey on Social Media Influence Environment and Influencers Identification

Gammoudi, Feriel; Sendi, Mondher; Omri, Mohamed Nazih

doi:10.1007/s13278-022-00972-y

Cited by 9 publications

(3 citation statements)

References 99 publications

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“…Surveyed reviews from users in social networks can be categorized into several distinct types based on the properties that [37], [38] reviews exhibit. The class categorization problem can be solved by considering two categories: the two-class problem and the multi-class problem.…”

Section: Proposed Modelmentioning

confidence: 99%

Machine learning-based approach for sentiment analysis from recorded social media movie reviews

Jassim,

Abd,

Omri

2023

Preprint

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The goal of Sentiment Analysis (SA), especially in social media, is to identify useful information in large amounts of unstructured data from many different sources. Text mining involves extracting revision texts and categorizing them by positive and negative attitudes. However, the value of the extracted features also lies in those that contribute most to the classification process. That's where dimensional reduction comes into play to remove noise and reduce the high area of the feature while maintaining the required accuracy. As part of this work, we propose a new approach for extracting words from a specific text and then classifying them. Thanks to a phase of pre-processing and extraction of words depending on the frequency. For this purpose, we considered a test group database of 50,000 film reviews, of which 25,000 were rated positive and 25,000 negatives. We have selected 4,000 words that have an impact on the feelings of the documents, To predict the rating which is based on a textual customer review. We evaluated the algorithms we proposed, as part of this approach, and compared the effectiveness of the following machine learning techniques as classifiers for SA: extreme gradient boosting (XGBoost), artificial neurons (ANN), and stochastic gradient descent (SGD). with a vectorial construction by applying the techniques of ”Term Frequency” (TF) and that of the ”Term frequency-Inverse Document Frequency” (TF-IDF). We noticed that our approach based on the SGD algorithm recorded the best performance. Indeed, we recorded an accuracy of 88.093\%, a precision of 89\%, a recall of 86\%, and an F1-score of 88\%. \textbf{Keywords :} Sentiment analysis; Opinion mining; movie review; classification; Machine learning; XGBoost; Artificial neural network; stochastic gradient descent.

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Section: Proposed Modelmentioning

confidence: 99%

Machine learning-based approach for sentiment analysis from recorded social media movie reviews

Jassim,

Abd,

Omri

2023

Preprint

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“…The authors in Gammoudi et al. ( 2022 ) presented a literature review of influencer identification on social media. Fake news and rumors are one of the principal influences on user credibility Gammoudi et al.…”

Section: Introductionmentioning

confidence: 99%

“…Fake news and rumors are one of the principal influences on user credibility Gammoudi et al. ( 2022 ). The evaluation of the credibility of sources, messages, and media represents an important topic to study Metzger et al.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based credibility conversation detection approaches from social network

Fadhli

Hlaoua

Omri

2023

Soc. Netw. Anal. Min.

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In recent years, the social networks that have become most exploited sources of information, such as Facebook, Instagram, LinkedIn, and Twitter, have been considered the main sources of non-credible information. False information on these social networks has a negative impact on the credibility of conversations. In this article, we propose a new deep learning-based credibility conversation detection approach in social network environments, called CreCDA. CreCDA is based on: (i) the combination of post and user features in order to detect credible and non-credible conversations; (ii) the integration of multi-dense layers to represent features more deeply and to improve the results; (iii) sentiment calculation based on the aggregation of tweets. In order to study the performance of our approach, we have used the standard PHEME dataset. We compared our approach with the main approaches we have studied in the literature. The results of this evaluation show the effectiveness of sentiment analysis and the combination of text and user levels to analyze conversation credibility. We recorded the mean precision of credible and non-credible conversations at 79%, the mean recall at 79%, the mean F1-score at 79%, the mean accuracy at 81%, and the mean G-Mean at 79%.

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