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A graph neural network-based machine learning model for sentiment polarity and behavior identification of COVID patients

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2025年第4期19卷 687-696页

作者： Srivastava, Shobhit Chakraborty, Chinmay Sarkar, Mrinal Kanti Univ Engn & Management Jaipur Rajasthan India BIT Mesra Mesra Jharkhand India

Sentiment analysis of people's opinions finds widespread application in numerous business and decision-making situations. Despite social media's informal nature for sharing viewpoints, it has now become a prevalent tool in various business and decision-making contexts. Sentiment analysis, also known as opinion mining, encompasses the utilization of techniques such as text analysis, computational linguistics, natural language processing, and even biometrics to analyze and interpret sentiments, opinions, and emotions expressed within textual data. Subsequently, we conducted an in-depth analysis of this data using a diverse range of machine learning algorithms, including Naive Bayes, Support Vector Machine (SVM), and decision trees. We are proposing a new machine learning model, called a hybrid feature selection-based model, for identifying the sentiment polarity of statements. This model is different from other machine learning models because of its unique architecture. It uses 16 different linguistic features in its hidden layers. To validate and fine-tune the behavior of this algorithm, we employed the Analytic Hierarchy Process method. This technique has been utilized in a wide variety of research projects, and it has also been combined with other methodologies to solve decision-making challenges. By integrating various linguistic features, our model demonstrates enhanced performance compared to other state-of-the-art models, resulting in an improvement of up to 2.8% in Accuracy and 3% in terms of the weighted F1-score.

关键词： Sentiment analysis Twitter Social networks analysis COVID-19 classification and clustering Analytic hierarchy process

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Robust ranking by ensembling of diverse models and assessment metrics

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2023年第1期93卷 77-102页

作者： Tomal, Jabed H. Welch, William J. Zamar, Ruben H. Thompson Rivers Univ Dept Math & Stat Kamloops BC V2C 0C8 Canada Univ British Columbia Dept Stat Vancouver BC Canada

We propose an ensemble of classification models formed using different assessment metrics. For a given metric, a classifier performs feature selection and combines models based on different subsets of feature variables which we call phalanxes. This first step, which employs the algorithm of phalanx formation, identifies strong and diverse subsets of feature variables. A second phase of ensembling aggregates classifiers across diverse assessment metrics. The proposed method is applied to protein homology data to mine homologous proteins, where the feature variables used for developing classifiers are various measures of similarity scores of proteins, and found robust for ranking both evolutionary close and distant homologous proteins.

关键词： Machine learning classification and clustering data mining feature selection robustness

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Hypergraph Learning: Methods and Practices

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第5期44卷 2548-2566页

作者： Gao, Yue Zhang, Zizhao Lin, Haojie Zhao, Xibin Du, Shaoyi Zou, Changqing Tsinghua Univ BNRist KLISS Sch Software Beijing 100084 Peoples R China Tsinghua Univ THUIBCS Beijing 100084 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Coll Artificial Intelligence Xian 710049 Shaanxi Peoples R China Sun Yat Sen Univ Guangzhou 510275 Peoples R China

Hypergraph learning is a technique for conducting learning on a hypergraph structure. In recent years, hypergraph learning has attracted increasing attention due to its flexibility and capability in modeling complex data correlation. In this paper, we first systematically review existing literature regarding hypergraph generation, including distance-based, representation-based, attribute-based, and network-based approaches. Then, we introduce the existing learning methods on a hypergraph, including transductive hypergraph learning, inductive hypergraph learning, hypergraph structure updating, and multi-modal hypergraph learning. After that, we present a tensor-based dynamic hypergraph representation and learning framework that can effectively describe high-order correlation in a hypergraph. To study the effectiveness and efficiency of hypergraph generation and learning methods, we conduct comprehensive evaluations on several typical applications, including object and action recognition, Microblog sentiment prediction, and clustering. In addition, we contribute a hypergraph learning development toolkit called THU-HyperG.

关键词： Learning systems Correlation Data models Laplace equations Three-dimensional displays Brain modeling Task analysis Hypergraph learning hypergraph generation hypergraph learning tool tensor-based dynamic hypergraph learning classification and clustering

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Application of Graph Document Model for classification of Agricultural Scientific Papers in Polish 21st

Application of Graph Document Model for Classification of Ag...

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21st International Conference on Computer Information Systems and Industrial Management (CISIM)

作者： Karwowski, Waldemar Wrzeciono, Piotr Warsaw Univ Life Sci Inst Informat Technol Nowoursynowska 166 PL-02787 Warsaw Poland

ISBN: (纸本)9783031105395;9783031105388

In the paper, the problem of classification of texts in the Polish language based on graph document model is discussed. A set of agricultural scientific texts in the Polish language are normalized and converted into directed graphs with words as vertices. Paths in the graph are assigned weights depending on how often words appear next to each other. The phrases with the highest weight are selected to represent the particular text in every graph. Only phrases with nouns, a verb, or an adjective were selected to use semantics connected with the relationship between different parts of speech. Methods for determining the similarity of phrases and documents were discussed and appropriate formulas were defined. For the representation of a document as a set of phrases, similarity between texts are calculated. The documents have been used to create an undirected graph whose edge weights equal the similarity values. On this basis, the documents are classified with the Chinese Whispers clustering algorithm. A series of experiments were performed for different versions of document similarity and different versions of the algorithm. In the final part it was concluded that increasing the length of phrases does not improve the results, but taking into account the grammatical category of words has a positive effect. Moreover, for clustering, the number of selected edges is more important than changes in the formulas for calculating weights.

关键词： Knowledge management Graph databases classification and clustering Text analysis

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Exploring the Open World Using Incremental Extreme Value Machines 26

Exploring the Open World Using Incremental Extreme Value Mac...

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26th International Conference on Pattern Recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Koch, Tobias Liebezeit, Felix Riess, Christian Christlein, Vincent Koehler, Thomas E Solut GmbH Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Erlangen Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Dynamic environments require adaptive applications. One particular machine learning problem in dynamic environments is open world recognition. It characterizes a continuously changing domain where only some classes are seen in one batch of the training data and such batches can only be learned incrementally. Open world recognition is a demanding task that is, to the best of our knowledge, addressed by only a few methods. This work introduces a modification of the widely known Extreme Value Machine (EVM) to enable open world recognition. Our proposed method extends the EVM with a partial model fitting function by neglecting unaffected space during an update. This reduces the training time by a factor of 28. In addition, we provide a modified model reduction using weighted maximum K-set cover to strictly bound the model complexity and reduce the computational effort by a factor of 3.5 from 2.1 s to 0.6 s. In our experiments, we rigorously evaluate openness with two novel evaluation protocols. The proposed method achieves superior accuracy of about 12 % and computational efficiency in the tasks of image classification and face recognition.

关键词： classification and clustering online learning and continual learning

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Illumination Depth

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2021年第1期30卷 78-90页

作者： Nagy, Stanislav Dvorak, Jiri Charles Univ Prague Fac Math & Phys Dept Probabil & Math Stat Sokolovska 83 Prague 18675 8 Czech Republic

The concept of illumination bodies studied in convex geometry is used to amend the halfspace depth for multivariate data. The proposed notion of illumination enables finer resolution of the sample points, naturally breaks ties in the associated depth-based ordering, and introduces a depth-like function for points outside the convex hull of the support of the probability measure. The illumination is, in a certain sense, dual to the halfspace depth mapping, and shares the majority of its beneficial properties. It is affine invariant, robust, uniformly consistent, and aligns well with common probability distributions. Supplementary materials for this article are available online.

关键词： classification and clustering Halfspace depth Illumination Multivariate analysis Outlier detection Tukey depth

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MARKOV-SWITCHING STATE SPACE MODELS FOR UNCOVERING MUSICAL INTERPRETATION

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ANNALS OF APPLIED STATISTICS 2021年第3期15卷 1147-1170页

作者： McDonald, Daniel J. McBride, Michael Gu, Yupeng Raphael, Christopher Univ British Columbia Dept Stat Vancouver BC Canada Indiana Univ Dept Stat Indianapolis IN 46204 USA Indiana Univ Sch Informat Comp & Engn Indianapolis IN 46204 USA

For concertgoers, musical interpretation is the most important factor in determining whether or not we enjoy a classical performance. Every performance includes mistakes-intonation issues, a lost note, an unpleasant sound-but these are all easily forgotten (or unnoticed) when a performer engages her audience, imbuing a piece with novel emotional content beyond the vague instructions inscribed on the printed page. In this research we use data from the CHARM Mazurka Project-46 professional recordings of Chopin's Mazurka Op. 68 No. 3 by consummate artists-with the goal of elucidating musically interpretable performance decisions. We focus specifically on each performer's use of tempo by examining the interonset intervals of the note attacks in the recording. To explain these tempo decisions, we develop a switching state space model and estimate it by maximum likelihood, combined with prior information gained frommusic theory and performance practice. We use the estimated parameters to quantitatively describe individual performance decisions and compare recordings. These comparisons suggest methods for informing music instruction, discovering listening preferences and analyzing performances.

关键词： classification and clustering Kalman filter hidden Markov model

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Combining artificial intelligence and expert content analysis to explore radical views on twitter: Case study on far-right discourse

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JOURNAL OF CLEANER PRODUCTION 2022年 362卷

作者： Ajala, Imene Feroze, Shanaz El Barachi, May Oroumchian, Farhad Mathew, Sujith Yasin, Rand Lutfi, Saad Univ Wollongong Dubai Sch Humanities Social Sci & Hlth Box 20183Block 15 Dubai U Arab Emirates Univ Wollongong Dubai Fac Engn & Informat Sci Box 20183Block 15 Dubai U Arab Emirates Zayed Univ Coll Technol Innovat Dubai Int Acad City Dubai U Arab Emirates

Public opinion is among the critical types of information that often inform policy changes and political strategies. Extreme public opinion is of particular importance due to the potential it has in leading to radical behaviors and violent actions. The monitoring and analysis of extreme public opinion are therefore of great interest to government officials seeking to maintain public order and preempt violent actions. Radicalization is the process employed to influence individuals to develop extreme views and behaviors. Due to their global reach and popularity, social media platforms are used tools for recruitment and radicalization. Recently, data mining and natural language processing techniques have been employed for the detection of extremism and radicalization online. However, the existing approaches focus on the classification of individuals as extremists or non extremists, thus simplifying the concept of radicalization to a binary problem. Such approaches fail to capture the nuances and the complexity of the radicalization process that is influenced by many aspects such as peer pressure, social interactions, and the impact of prominent figures. Aiming at addressing those limitations, this work proposed a sophisticated approach for the analysis of extreme views expressed on social media. The proposed approach combines the power of artificial intelligence and natural language processing techniques with expert content analysis to achieve a fine-grained and detailed analysis of Twitter extremist content related to the Far-right ideology as a case study. A dataset of over 259,000 tweets collected over five years was used to test our approach, leading to sophisticated analytics and insights about Far-right extremism. The proposed approach can serve as a powerful decision support tool for governments for the analysis of extreme public opinion that is expressed online, and open the door for more effective and responsive decision making.

关键词： Radical views Social media analytics Far-right Opinion leaders classification and clustering

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Prediction of COVID-19 using long short-term memory by integrating principal component analysis and clustering techniques

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Informatics in Medicine Unlocked 2022年 31卷 100990页

作者： Ilu, Saratu Yusuf Rajesh, Prasad Mohammed, Hassan African University of Science and Technology Abuja Nigeria Bayero University Kano Nigeria

Severe acute respiratory syndrome coronavirus (SARS-COV) is a major family of viruses that cause infections in both animals and humans, including common cold, coronavirus disease (COVID-19), severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome. This study primarily aims to predict the number of COVID-19 positive cases in 36 states of Nigeria using a long short-term memory (LSTM) algorithm of deep learning. The proposed approach employs K-means clustering to detect outliers and principal component analysis (PCA) to select important features from the dataset. The LSTM was chosen because of its non-linear characteristics to handle the dataset. As COVID-19 cases follow non-linear characteristics, LSTM is the most suitable algorithm for predicting their numbers. For comparison, several types of machine learning algorithms, such as naive Bayes, XG-boost, and SVM, were employed. After the comparison, LSTM was observed to be superior among all algorithms. © 2022 The Authors

关键词： classification and clustering COVID-19 LSTM Machine learning

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A Cluster-Based Improved Expectation Maximization Framework for Identification of Somatic Gene Clusters 2nd

A Cluster-Based Improved Expectation Maximization Framework ...

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2nd International Conference on Computing, Communications and Data Engineering (CCODE)

作者： Chokka, Anuradha Rani, K. Sandhya Sri Padmavati Mahila Visvavidyalayam Dept Comp Sci Tirupati Andhra Pradesh India

ISBN: (纸本)9789811501357;9789811501340

The early identification of cancer disease is the key objective of this paper. Machine learning algorithm's contribution is significant for early recognition of somatic mutations in cancer patients. So, the study of classification and clustering play a vital role in predicting the somatic mutations patterns. As the size of gene variants and somatic mutation patterns in the tumor increases, it is essential and effective to predict the disease patterns using the machine learning models. In this proposed work, a novel framework is designed and implemented on the somatic cancer datasets. In this work, somatic mutation patterns are clustered using the related features of gene sequences by using the proposed improved expectation maximization (IEM) model. On each cluster, AdaBoost classifier is applied to classify somatic mutations patterns. Experimental results proved that the proposed clustering algorithm IEM is better than the traditional approaches in terms of cluster quality rate. The overall classification accuracy for all the clusters is also satisfactory.

关键词： Somatic mutations patterns Improved expectation maximization classification and clustering

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