Vector representation based on a supervised codebook for Nepali documents classification.

Multiple studies have investigated bibliometric features and uncategorized scholarly documents for the influential scholarly document prediction task. In this paper, we describe our work that attempts to go beyond bibliometric metadata to predict influential scholarly documents. Furthermore, this work also examines the influential scholarly document prediction task over categorized scholarly documents. We also introduce a new approach to enhance the document representation method with a domain-independent knowledge graph to find the influential scholarly document using categorized scholarly content. As the input collection, we use the WHO corpus with scholarly documents on the theme of COVID-19. This study examines different document representation methods for machine learning, including TF-IDF, BOW, and embedding-based language models (BERT). The TF-IDF document representation method works better than others. From various machine learning methods tested, logistic regression outperformed the other for scholarly document category classification, and the random forest algorithm obtained the best results for influential scholarly document prediction, with the help of a domain-independent knowledge graph, specifically DBpedia, to enhance the document representation method for predicting influential scholarly documents with categorical scholarly content. In this case, our study combines state-of-the-art machine learning methods with the BOW document representation method. We also enhance the BOW document representation with the direct type (RDF type) and unqualified relation from DBpedia. From this experiment, we did not find any impact of the enhanced document representation for the scholarly document category classification. We found an effect in the influential scholarly document prediction with categorical data.

Latent Dirichlet Allocation (LDA), as the most popular topic model, models documents as mixtures of topics and topics as mixtures of words. Topic mixture well represents documents while words mixture extracts meaningful topics from a corpus. The nature of LDA makes it a powerful tool in documents organizing and corpus summarizing. One limitation of LDA is that its performance depends heavily on the priors. Researchers show priors matters in LDA and propose methods to learn the priors for better modeling, regardless of using symmetric priors. However, LDA modeling ability does not necessarily consent with the performance of LDA in documents representation and topic extraction. In this paper, we propose a novel prior setting for LDA. The setting improves LDA in both documents representation and topic extraction performance. We experiment to compare our setting with symmetric priors and previously proposed priors that enhances modeling ability. Experiments on the topic quality show that LDA with our prior setting extracts better topics than LDA with other kinds of prior settings. We compare LDA document representation ability through tasks such as document clustering and document classification. These experiments demonstrate LDA with our proposed priors represents document better. Moreover, our analyses also reveal that better modeling does not necessarily lead to better performance in documents representation and topic extraction.

Inferential representation of science documents

This paper proposes an approach for aspect-based sentiment analysis of Arabic social data, especially the considerable text corpus generated through communications on X (formerly known as Twitter) for expressing opinions in Arabic-language tweets during the COVID-19 pandemic. The proposed approach examines the performance of several pre-trained predictive and autoregressive language models; namely, Bidirectional Encoder Representations from Transformers (BERT) and XLNet, along with topic modeling algorithms; namely, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), for aspect-based sentiment analysis of online Arabic text. In addition, Bidirectional Long Short Term Memory (Bi-LSTM) deep learning model is used to classify the extracted aspects from online reviews. Obtained experimental results indicate that the combined XLNet-NMF model outperforms other implemented state-of-the-art methods through improving the feature extraction of unstructured social media text with achieving values of 0.946 and 0.938, for average sentiment classification accuracy and F-measure, respectively.

Latent Dirichlet Allocation (LDA) is a commonly used topic modeling method for text analysis and mining. Standard LDA treats documents as bags of words, ignoring the syntactic structures of sentences. In this paper, we propose a hybrid model that embeds hidden Markov models (HMMs) within LDA topics to jointly model both the topics and the syntactic structures within each topic. Our model is general and subsumes standard LDA and HMM as special cases. Compared with standard LDA and HMM, our model can simultaneously discover both topic-specific content words and background functional words shared among topics. Our model can also automatically separate content words that play different roles within a topic. Using perplexity as evaluation metric, our model returns lower perplexity for unseen test documents compared with standard LDA, which shows its better generalization power than LDA.

Data-driven innovation has received increasing attention, which explores big data technologies to gain more insights and advantages for product design. In user experience (UX) based design innovation, user-generated data and archived design documents are two valuable resources for various design activities such as identifying opportunities and generating design ideas. However, these two resources are usually isolated in different systems. Additionally, design information typically represented based on functional aspects is limited for UX-oriented design. To facilitate experience-oriented design activities, we propose a twin data-driven approach to integrate UX data and archived design documents. In particular, we aim to extract UX concepts from product reviews and design concepts from patents respectively and to discover associations between the extracted concepts. First, a UX-integrated design information representation model is proposed to associate capabilities with key elements of UX at the concept, category, and aspect levels of information. Based on this model, a twin data-driven approach is developed to bridge experience information and design information. It contains three steps: experience aspect identification using an attention-based LSTM (Long short-term memory) network, design information categorization based on topic clustering using BERT (Bidirectional Encoder Representations from Transformers) and LAD (Latent Dirichlet allocation) model, and experience needs and design information integration by leveraging word embedding techniques to measure concept similarity. A case study using healthcare-related experience and design information has demonstrated the feasibility and effectiveness of this approach.

Sentiment analysis of news media articles is essential for understanding the dynamics of conflict and cooperation in transboundary rivers. However, it is not known which machine learning model(s) can best meet the requirement of sentiment analysis for transboundary rivers. This study presents a comparative examination of ten machine learning models commonly used in the field of text sentiment analysis, including K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting Decision Tree, Extreme Gradient Boosting, Multilayer Perceptron, Long Short-Term Memory and Bidirectional Encoder Representations from Transformers, for five-class sentiment classification of 9382 news articles (1977–2022) attending to transboundary water conflict and cooperation. By evaluating their performance in terms of accuracy, precision, recall and F1-score, the Bidirectional Encoder Representations from Transformers (BERT) model demonstrated good overall performance and prediction capabilities for news articles with conflictive sentiments. By comparing with the AFINN sentiment dictionary, BERT showed superior performance in the prediction and identification of conflictive sentiment labels. And by validating against historical water events in the three river basins, BERT performed best in the Indus River basin. The findings of this study hold significant implications for government agencies in transboundary rivers, allowing them to promptly assess and respond to public sentiment, thereby preventing water conflict and promoting water cooperation.

Scene classification from images is a challenging problem in computer vision due to its significant variability of scale, illumination, and view. Recently, Latent Dirichlet Allocation (LDA) model has grown popular in computer vision field, especially in scene labeling and classification. However, the effectiveness of the LDA model for the scene classification has not yet been addressed thoroughly. Especially, there is little experimental evaluation on the model's performance for different types of features. Fusion of multiple types of features is usually necessary in the scene classification due to the complexity of scene images. In this paper, we investigate the effectiveness of the LDA model in scene classification by using 7 types of features (i.e. uniform grid based interest points, Harris corner based interest points, scale invariant feature transform (SIFT), texture, shape, color, and location) and their various combinations. Furthermore, we compare the performance of the LDA model with Support Vector Machine (SVM) classifier. All experiments are performed on the UIUC Sport Scene database. The experiments demonstrate that the performance of the LDA model 1) is significantly lower than the SVM classifier for the scene classification over different types of features; and 2) decreases by fusing multiple features while improvement shown in SVM classifier.

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

Background: Mobile app usage is increasing in the digital age, with Ride-Hailing app becoming the primary example of this trend. To obtain valuable understanding of how people perceive and interact with mobile app, user reviews on platforms such as Google Play are usually analyzed. This analysis can assist developers to identify areas for improvement in both Ride-hailing and Google Play App. A promising method that can be used to analyze user perception in this instance is Aspect-Based Sentiment Analysis (ABSA). Objective: This research aimed to apply ABSA to user reviews using Bidirectional Encoder Representations from Transformers (BERT) models. In this context, aspect identification and topic modeling were performed by using Latent Dirichlet Allocation (LDA). The model extracted topics from the reviews and used Generative Artificial Intelligence (GenAI) to define the aspects of the topics to further enhance the analysis. For consistency and accuracy, the method included sentiment annotation by a human annotator. Methods: A total of two datasets were used in this research, with the first collected by scraping user reviews of Ride-Hailing App while the second was obtained from Kaggle, and to identify relevant topics, modeling was performed using LDA. These topics were then categorized into aspects using GenAI, covering areas, such as customer experience, service, payment, app features, task management, and event management. Subsequently, sentiment labeling was conducted using human annotators to provide a reliable baseline. BERT model was then used to classify sentiment with aspect hints, and the evaluation included calculations of accuracy, precision, recall, and F1-score. Results: The results showed that BERT model achieved the highest accuracy of 97% in sentiment analysis across all datasets. Conclusion: This research provided valuable understanding of user experience and established a strong ABSA framework for analyzing user reviews using LDA, Aspect Annotation, GenAI, and BERT sentiment models. Future research could expand this method to other app categories and incorporate real-time ABSA for continuous monitoring and dynamic feedback. Keywords: User Reviews, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Topic Modeling, Generative Artificial Intelligence (GenAI)

Customer satisfaction service match and service quality-based blockchain cloud manufacturing

As social media and online social networking sites have emerged as essential channels for everyday social interactions, researchers are increasingly turning to conversational social media as a valuable alternative source of insights into public sentiment and behavior during times of crisis. In a similar vein, our study investigates the utility of using conversational social media data for analyzing public sentiment related to the COVID-19 pandemic. Our research question is, to what extent can machine learning models based on conversational social media data help gauge public sentiment during a pandemic? To evaluate the performance of various machine learning algorithms in sentiment analysis tasks, we used a Twitter dataset and compared their effectiveness using eight different models (i.e., Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Extreme Gradient Boosting (XGBoost), Logistic Regression, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT)). The results show that among the eight models, the top three models are BERT, LSTM, and SVM, achieving overall accuracies of 87.44%, 81.94%, and 80.02%, respectively. Employing sentiment analysis models on pandemic-related social media posts allows us to gain insights into of human emotions and sentiments during public health crises like a pandemic.

Emotion detection plays a vital role in understanding human sentiments and behaviors across various applications, including customer feedback analysis and mental health monitoring. This research assesses the efficiency of different algorithms for machine learning in detecting emotions in text data. A meticulously curated dataset is utilized for the study. The research compares conventional models like Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), and Naive Bayes (NB) with deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Bidirectional Encoder Representations from Transformers (BERT). The performance of each algorithm is assessed using accuracy, precision, recall, and F1 score. BERT exhibits superiority over other models, achieving the maximum accuracy of 0.8867 and F1 Score of 0.8871. CNN and SVM also display commendable performance. While the traditional models perform adequately, they are surpassed by deep learning models, with Naive Bayes showing the lowest metrics. This study underscores the significance of selecting models based on specific application requirements, taking into account factors like interpretability and efficiency. Future research endeavors may explore multimodal approaches, model interpretability, bias reduction, and real-time applications, thereby contributing to the advancement of emotion detection in text.

Fake news occurs when a news article is intentionally released and subsequently shown to be false. This can negatively impact on an individual’s or indeed society’s comprehension of topics and shape public opinion on major events, e.g. elections. With the ubiquity of social media and fake news websites, people are increasingly exposed to fake news. In this article, we explore and compare the performance of fake news detection approaches using both machine and deep learning methods. Specifically we explore Logistic Regression, Support Vector Machines (SVM), Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) and compare the results with human crowds ability to distinguish real vs fake news. We utilise over 8.5m records from FakeNewsCorpus and 23k records from FakeNewsNet. The results shown that BERT achieved the best overall accuracy at 82.5%. Groups of individual assessors achieved an accuracy of 79%, whilst individuals varied significantly between 60-80% in their ability to distinguish real vs fake news.

This study introduces the “framing element” method, an alternative approach to computational news framing detection. Rooted in a constructionist framing analysis framework, it identifies frames as packages of framing elements, including actors (individuals and organizations) and topics, extending beyond topic-focused methods in prior unsupervised analyses. Compared with latent Dirichlet allocation (LDA)- and Bidirectional Encoder Representations from Transformers (BERT)-based approaches on 1,300 U.S. gun violence news articles, this method addresses LDA’s limitations by focusing on high-level framing elements rather than keywords and is less labor-intensive than BERT-based supervised learning. Supporting both inductive and deductive analyses, it achieves comparable results to LDA while uncovering a previously unidentified gun violence frame.