TITANIS: A Tool for Intelligent Text Analysis in Social Media

Smirnov, Ivan V.; Stankevich, Maxim; Kuznetsova, Yu. M.; Suvorova, Margarita; Larionov, Daniil; Никитина, Елена; Savelov, Mikhail; Grigoriev, Oleg

doi:10.1007/978-3-030-86855-0_16

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…The parameters of the collected data after cleaning are presented in Table 1. In the next step, each comment was analyzed using TITANIS [71], which computes multi-level linguistic markers of texts. Those text markers that required a morphological annotation of words were based on the results of a MyStem [72] analysis.…”

Section: Data and Text Processingmentioning

confidence: 99%

Modeling Seasonality of Emotional Tension in Social Media

Nosov,

Kuznetsova,

Stankevich

et al. 2023

Computers

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Social media has become an almost unlimited resource for studying social processes. Seasonality is a phenomenon that significantly affects many physical and mental states. Modeling collective emotional seasonal changes is a challenging task for the technical, social, and humanities sciences. This is due to the laboriousness and complexity of obtaining a sufficient amount of data, processing and evaluating them, and presenting the results. At the same time, understanding the annual dynamics of collective sentiment provides us with important insights into collective behavior, especially in various crises or disasters. In our study, we propose a scheme for identifying and evaluating signs of the seasonal rise and fall of emotional tension based on social media texts. The analysis is based on Russian-language comments in VKontakte social network communities devoted to city news and the events of a small town in the Nizhny Novgorod region, Russia. Workflow steps include a statistical method for categorizing data, exploratory analysis to identify common patterns, data aggregation for modeling seasonal changes, the identification of typical data properties through clustering, and the formulation and validation of seasonality criteria. As a result of seasonality modeling, it is shown that the calendar seasonal model corresponds to the data, and the dynamics of emotional tension correlate with the seasons. The proposed methodology is useful for a wide range of social practice issues, such as monitoring public opinion or assessing irregular shifts in mass emotions.

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Section: Data and Text Processingmentioning

confidence: 99%

Modeling Seasonality of Emotional Tension in Social Media

Nosov,

Kuznetsova,

Stankevich

et al. 2023

Computers

Self Cite

View full text Add to dashboard Cite

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“…The situation component of the OSPC model refers to the external context in which an organization operates, and its PR efforts are conducted [17]. AI can significantly influence situational factors, as it allows organizations to monitor and analyze vast amounts of data to better understand the external environment and respond proactively to emerging trends and issues [26] In crisis communication, AIpowered tools, such as Brand24 and Mention, can help organizations identify potential crises in realtime and develop rapid response strategies to reduce negative impacts on their reputation [27] [28]. Additionally, AI-driven sentiment analysis tools, like Brand24 and IBM Watson Natural Language, can assist organizations in gauging public opinion and detecting shifts in sentiment, enabling them to adapt their PR strategies to changing situations [25] [29].…”

Section: Organization Levelmentioning

confidence: 99%

Examining the Influence of Artificial Intelligence on Public Relations: Insights from the Organization-Situation-Public-Communication (OSPC) Model

Jeong¹,

Park²

2023

APJCRI

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The growing presence of Artificial Intelligence (AI) within the Public Relations (PR) sector is an undeniable reality. Although still early in the journey towards widespread adoption, organizations are progressively infusing AI into their PR approaches. Yet, there's a spectrum of understanding and implementation across practitioners. The objective of this research was to shed light on AI's influence on PR within the context of the Organization-Situation-Public-Communication (OSPC) model. Employing a comprehensive literature review as the research design, we examined a vast array of scholarly articles and industry reports to collect data on AI-driven tools, their application in PR, and the implications at each level of the OSPC model. Results of the study revealed that AI-driven tools were enhancing efficiency, personalizing communications, adapting to situational factors, and cultivating relationships with publics. These advancements, however, came with potential risks, challenges, and ethical considerations that require further investigation. Specifically, AI-driven automation can sometimes overshadow human expertise, raising concerns about trust and transparency in PR communications. The study concluded that there is a critical need for PR practitioners to strike an optimal balance between AI-driven automation and human expertise. Moreover, PR practitioners are encouraged to continuously update their skills and competencies, particularly in data analytics and AI, and adopt transparent communication practices. This study offers valuable insights for practitioners and researchers to effectively navigate the challenges and opportunities presented by this rapidly evolving technology, and to develop more informed and ethical AI-driven PR strategies.

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“…To perform classification, we used 70%-30% train-test split with respect to governmental and oppositional classes to keep proportion balance for 3-class classification. The dataset contains features that were used to analyze the reaction of the population to COVID-19 lockdown in Pikabu social network [41]. These features can be divided into several categories.…”

Section: Datasetsmentioning

confidence: 99%

Random Kernel Forests

Devyatkin

Grigoriev

2022

IEEE Access

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Random forests of axis-parallel decision trees still show competitive accuracy in various tasks; however, they have drawbacks that limit their applicability. Namely, they perform poorly for multidimensional sparse data that is frequently input in many text and image processing tasks. A straightforward solution is to create forests of decision trees with oblique splits; however, most training approaches have low performance or are hard to tune. Besides, those ensembles appear unstable and easily overfit, so they should be regularized to find the trade-off between complexity and generalization. This paper proposes an algorithm to train kernel decision trees and random forests. At the top level, this algorithm follows a common greedy procedure to train binary decision trees; however, it produces quasi-optimal linear and kernel splits at the decision stump level. At each stump, the algorithm finds a quasi-optimal distribution of classes to subtrees and trains this stump via optimization of an SVM-like loss function with a margin re-scaling approach, which helps simultaneously optimize the margin between subtree data and arbitrary impurity criteria. We also try to reveal uniform stability-based generalization bounds for those forests and apply them to select the regularization technique. The obtained bounds explicitly consider primary hyperparameters of forests, trees, and decision stumps. The bounds also show a relationship between outliers in decision tree structure, stability and generalization, and how forests smooth these outliers. We performed an experimental evaluation on several tasks, such as studying the reaction of social media users, image recognition, and bank scoring. The experimental results show that the proposed algorithm trains ensembles, which outperform other oblique or kernel forests in many commonly-recognized datasets. Namely, the proposed algorithm shows 99.1% accuracy on MNIST and 58.1% on CIFAR-10. It has been confirmed that the selected regularization technique helps reduce overfitting on several datasets. Therefore, the proposed algorithm may be considered a small step toward customized and regularized ensembles of oblique or kernel trees that keep reasonable training performance on large datasets. We believe that the proposed algorithm can be utilized as a construction element of approaches to training context-aware models on complex problems with integer inputs and outputs, such as text mining and image recognition.INDEX TERMS Generalization bounds, kernel splits, kernel forests, random forests, random uniform stability, regularization, slack re-scaling.

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TITANIS: A Tool for Intelligent Text Analysis in Social Media

Cited by 8 publications

References 38 publications

Modeling Seasonality of Emotional Tension in Social Media

Modeling Seasonality of Emotional Tension in Social Media

Examining the Influence of Artificial Intelligence on Public Relations: Insights from the Organization-Situation-Public-Communication (OSPC) Model

Random Kernel Forests

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