A framework for investigating the dynamics of user and community sentiments in a social platform

Bonifazi, Gianluca; Cauteruccio, Francesco; Corradini, Enrico; Marchetti, Michele; Terracina, Giorgio; Ursino, Domenico; Virgili, Luca

doi:10.1016/j.datak.2023.102183

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…On the other hand, employees who are, on average, likely to email many colleagues outside their immediate clique are less likely to receive replies. These insights can generalize to understanding communication behavior on other small-world networks, such as social media platforms, where research on the virality of content [75,76], hot streaks in user popularity [77], and their consequences for user and community behavior [78,79] all discuss the importance of social network features in understanding and modeling communication cascades.…”

Section: Discussionmentioning

confidence: 88%

Building a Multimodal Classifier of Email Behavior: Towards a Social Network Understanding of Organizational Communication

Shah,

Jaidka,

Ungar

et al. 2023

Information

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Within organizational settings, communication dynamics are influenced by various factors, such as email content, historical interactions, and interpersonal relationships. We introduce the Email MultiModal Architecture (EMMA) to model these dynamics and predict future communication behavior. EMMA uses data related to an email sender’s social network, performance metrics, and peer endorsements to predict the probability of receiving an email response. Our primary analysis is based on a dataset of 0.6 million corporate emails from 4320 employees between 2012 and 2014. By integrating features that capture a sender’s organizational influence and likability within a multimodal structure, EMMA offers improved performance over models that rely solely on linguistic attributes. Our findings indicate that EMMA enhances email reply prediction accuracy by up to 12.5% compared to leading text-centric models. EMMA also demonstrates high accuracy on other email datasets, reinforcing its utility and generalizability in diverse contexts. Our findings recommend the need for multimodal approaches to better model communication patterns within organizations and teams and to better understand how relationships and histories shape communication trajectories.

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Section: Discussionmentioning

confidence: 88%

Building a Multimodal Classifier of Email Behavior: Towards a Social Network Understanding of Organizational Communication

Shah,

Jaidka,

Ungar

et al. 2023

Information

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“…As for the ERW-UIL framework, it performs not so well on sparse graphs, so before the ERW process, we need to rationally enhance the graph to eliminate the sparsity of the graph. And inspired by [21][22][23], we will try to integrate the structural feature with users' behavior features or sentiment features to enrich the users' features and improve the model's performance.…”

Section: Discussionmentioning

confidence: 99%

Novel Method of Edge-Removing Walk for Graph Representation in User Identity Linkage

Xie,

Zang,

et al. 2024

Electronics

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Random-walk-based graph representation methods have been widely applied in User Identity Linkage (UIL) tasks, which links overlapping users between two different social networks. It can help us to obtain more comprehensive portraits of criminals, which is helpful for improving cyberspace governance. Yet, random walk generates a large number of repeating sequences, causing unnecessary computation and storage overhead. This paper proposes a novel method called Edge-Removing Walk (ERW) that can replace random walk in random-walk-based models. It removes edges once they are walked in a walk round to capture the l−hop features without repetition, and it walks the whole graph for several rounds to capture the different kinds of paths starting from a specific node. Experiments proved that ERW can exponentially improve the efficiency for random-walk-based UIL models, even maintaining better performance. We finally generalize ERW into a general User Identity Linkage framework called ERW-UIL and verify its performance.

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“…One year later, in 2023, the authors extended their work and proposed a scalable framework for analyzing sentiment dynamics among users and communities on social media, focusing on community influence, sentiment evolution, and inter-community interactions. Their approach has been empirically validated through extensive experiments [11].…”

Section: Related Work 21 Sentimental Analysis In Social Mediamentioning

confidence: 99%

Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine

Louati,

Kariri

et al. 2023

Applied Sciences

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This study presents the development of a sentimental analysis system for high education students using Arabic text. There is a gap in the literature concerning understanding the perceptions and opinions of students in Saudi Arabia Universities regarding their education beyond COVID-19. The proposed SVM Sentimental Analysis for Arabic Students’ Course Reviews (SVM-SAA-SCR) algorithm is a general framework that involves collecting student reviews, preprocessing them, and using a machine learning model to classify them as positive, negative, or neutral. The suggested technique for preprocessing and classifying reviews includes steps such as collecting data, removing irrelevant information, tokenizing, removing stop words, stemming or lemmatization, and using pre-trained sentiment analysis models. The classifier is trained using the SVM algorithm and performance is evaluated using metrics such as accuracy, precision, and recall. Fine-tuning is done by adjusting parameters such as kernel type and regularization strength to optimize performance. A real dataset provided by the deanship of quality at Prince Sattam bin Abdulaziz University (PSAU) is used and contains students’ opinions on various aspects of their education. We also compared our algorithm with CAMeLBERT, a state-of-the-art Dialectal Arabic model. Our findings show that while the CAMeLBERT model classified 70.48% of the reviews as positive, our algorithm classified 69.62% as positive which proves the efficiency of the suggested SVM-SAA-SCR. The results of the proposed model provide valuable insights into the challenges and obstacles faced by Arab Universities post-COVID-19 and can help to improve their educational experience.

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A framework for investigating the dynamics of user and community sentiments in a social platform

Cited by 10 publications

References 31 publications

Building a Multimodal Classifier of Email Behavior: Towards a Social Network Understanding of Organizational Communication

Building a Multimodal Classifier of Email Behavior: Towards a Social Network Understanding of Organizational Communication

Novel Method of Edge-Removing Walk for Graph Representation in User Identity Linkage

Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine

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