RUemo—The Classification Framework for Russia-Ukraine War-Related Societal Emotions on Twitter through Machine Learning

Vyas, Piyush J.; Vyas, Gitika

doi:10.3390/a16020069

Cited by 24 publications

(14 citation statements)

References 55 publications

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“…As for our aim, in the last year, other works proposed sentiment, emotion, and/or intention analysis over user-generated content concerning the Russia-Ukraine conflict in order to deepen the war perception [39], by also using ML-based strategies [40] or defining proper models, as for the MF-CNN-BiLSTM model defined by Aslan [41]. Most of them exploit Twitter API and/or previously published tweets' datasets.…”

Section: Related Workmentioning

confidence: 99%

Analyzing the worldwide perception of the Russia-Ukraine conflict through Twitter

Breve,

Caruccio,

Cirillo

et al. 2024

J Big Data

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In this paper, we analyze the worldwide perception of the Russia-Ukraine conflict (RU conflict for short) on the Twitter platform. The study involved collecting over 17 million tweets written in 63 different languages and conducting a multi-language sentiment analysis, as well as an analysis of their geographical distribution and verification of their temporal relationship to daily events. Additionally, the study focused on analyzing the accounts producing pro-conflict tweets to evaluate the possible presence of bots. The results of the analysis showed that the war had a significant global impact on Twitter, with the volume of tweets increasing as the war’s threats materialized. There was a strong correlation between the succession of events, the volume of tweets, and the prevalence of a specific sentiment. Most tweets had a negative sentiment, while tweets with positive sentiment mainly contained support and hope for people directly involved in the conflict. Moreover, a bot detection analysis performed on the collected tweets revealed the presence of many accounts spreading tweets including pro-conflict hashtags that cannot be identified as real users. Overall, this study sheds light on the importance of social media in shaping public opinion during conflicts and highlights the need for reliable methods to detect bots.

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Section: Related Workmentioning

confidence: 99%

Analyzing the worldwide perception of the Russia-Ukraine conflict through Twitter

Breve,

Caruccio,

Cirillo

et al. 2024

J Big Data

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“…In addition, due to the unique characteristics of the MMKGs constructed for music, we propose an enhanced featurefusion method in MKGCN, which will be explained in more detail in the experiments section. It is worth noting that, in contrast to the multi-model work in [31], our work uses multi-modal data to enhance the embedding representation of users and items, rather than using multi-models to explore which machine learning approach is best suited for the downstream task of recommendation.…”

Section: Recommendations With Mmkgsmentioning

confidence: 99%

MKGCN: Multi-Modal Knowledge Graph Convolutional Network for Music Recommender Systems

Xiao-hui

et al. 2023

Electronics

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With the emergence of online music platforms, music recommender systems are becoming increasingly crucial in music information retrieval. Knowledge graphs (KGs) are a rich source of semantic information for entities and relations, allowing for improved modeling and analysis of entity relations to enhance recommendations. Existing research has primarily focused on the modeling and analysis of structural triples, while largely ignoring the representation and information processing capabilities of multi-modal data such as music videos and lyrics, which has hindered the improvement and user experience of music recommender systems. To address these issues, we propose a Multi-modal Knowledge Graph Convolutional Network (MKGCN) to enhance music recommendation by leveraging the multi-modal knowledge of music items and their high-order structural and semantic information. Specifically, there are three aggregators in MKGCN: the multi-modal aggregator aggregates the text, image, audio, and sentiment features of each music item in a multi-modal knowledge graph (MMKG); the user aggregator and item aggregator use graph convolutional networks to aggregate multi-hop neighboring nodes on MMKGs to model high-order representations of user preferences and music items, respectively. Finally, we utilize the aggregated embedding representations for recommendation. In training MKGCN, we adopt the ratio negative sampling strategy to generate high-quality negative samples. We construct four different-sized music MMKGs using the public dataset Last-FM and conduct extensive experiments on them. The experimental results demonstrate that MKGCN achieves significant improvements and outperforms several state-of-the-art baselines.

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“…User's sentiments and spirits are articulated through the information, which is primarily based on a shared object of concentration. These fragments of information have grown into informational treasure troves, providing numerous opportunities for analyzing consumer behavior, which is particularly useful in predicting product sales [2], stock market trends [3], and election results [4]. More than 300 million active users use X, which is one of the most widely used microblogging services [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Approach for Classification and Analysis of X Posts on Russia Ukraine War

Suman,

Varshney,

Chouhan

et al. 2024

IJCDS

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X (formerly Twitter) has become a vital source of information on various variety of social, political, and economic concerns, as a consequence of its growth and popularity which has resulted in an enormous number of people sharing their opinions on a wide range of areas. To determine people's emotions about the Russia-Ukraine war (RUW), this study examines trends in English-language tweets. In this work, we have engaged 34 countries to tweet opinions that produce a strong perception of the people about the war and message to the world what people famine from the countries and that affects their lives. To analyze positive and negative emotions in tweets, which are represented by hope and fear, the LSTM-CNN model is based on deep learning. A time series is calculated that correlates with the rate of recurrence of negative and positive tweets in different nations. Additionally, an approach based on the average of the neighborhood has been used for modelling and grouping the time series of various countries. The clustering method gives results as significant information, how people feel about this dispute and share their opinions about RUW is approached. When compare on different models on overall data that the 96% accuracy is Achieved by the LSTM-CNN model. 97.09% accuracy, is achieved when the comparing the tweets from the cluster 1 countries. When comparing the tweets from the cluster 2 countries the 99% accuracy, is achieved. 97% accuracy is achieved by comparing the tweets from cluster 3 countries. 97% accuracy, is achieved when the comparing the tweets from cluster 4 countries. 96% accuracy is achieved by the LSTM-CNN model when the comparing the tweets from cluster 5 countries by the different models. This research study helps the uninfluenced press members to have an impartial source of information for their reports and articles.

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RUemo—The Classification Framework for Russia-Ukraine War-Related Societal Emotions on Twitter through Machine Learning

Cited by 24 publications

References 55 publications

Analyzing the worldwide perception of the Russia-Ukraine conflict through Twitter

Analyzing the worldwide perception of the Russia-Ukraine conflict through Twitter

MKGCN: Multi-Modal Knowledge Graph Convolutional Network for Music Recommender Systems

Hybrid Deep Learning Approach for Classification and Analysis of X Posts on Russia Ukraine War

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