The paper deals with the analysis of conversation transcriptions between customers and agents in a call center of a customer care service. The objective is to support the analysis of text transcription of human-to-human conversations, to obtain repor...
The research presents the methodology of improving the accuracy in sentiment classification in the light of modelling the latent semantic relations (LSR). The objective of this methodology is to find ways of eliminating the limitations of the discrim...
This study explores the determinants of job satisfaction among IT industry employees in the U.S. and South Korea, focusing on how cultural and socio-economic contexts influence employee well-being and organizational sustainability. Given the high tur...
11 December 2024
Within the evolving field of sentiment analysis, the integration of topic modeling and association rule mining presents a promising yet underexplored method. This approach currently lacks an organized framework for maximizing insights that aid in dra...
Following the significant disruption of traditional teaching by the COVID-19 pandemic, gamified education—an approach integrating technology and cognitive strategies—has gained widespread attention and use among educators and learners. Th...
13 November 2022
Interactive Machine Learning (IML) can enable intelligent systems to interactively learn from their end-users, and is quickly becoming more and more relevant to many application domains. Although it places the human in the loop, interactions are most...
Traditional topic models are effective at uncovering patterns within large text corpora but often struggle with capturing the contextual nuances necessary for meaningful interpretation. As a result, these models may produce incoherent topics, making...
The accuracy of traditional topic models may be compromised due to the sparsity of co-occurring vocabulary in the corpus, whereas conventional word embedding models tend to excessively prioritize contextual semantic information and inadequately captu...
Customer sentiment analysis plays a pivotal role in the digital economy by offering comprehensive insights that inform strategic business decisions, optimize digital marketing initiatives, and improve overall customer satisfaction. We propose a large...
Image captioning is an important task in cross-modal research in numerous applications. Image captioning aims to capture the semantic content of an image and express it in a linguistically and contextually appropriate sentence. However, existing mode...
Bell nonlocality and Kochen–Specker contextuality are among the main topics in the foundations of quantum theory. Both of them are related to stronger-than-classical correlations, with the former usually referring to spatially separated systems...
This article presents a new conceptual approach for the interpretative topic modeling problem. It uses sentences as basic units of analysis, instead of words or n-grams, which are commonly used in the standard approaches.The proposed approach’s speci...
User comments often contain their most practical requirements. Using topic modeling of user comments, it is possible to classify and downscale text data, mine the information in user comments, and understand users’ requirements and preferences....
Existing approaches to social media sentiment analysis typically focus on static classification, offering limited foresight into how public opinion evolves. This study addresses that gap by introducing the Multi-Feature Sentiment-Driven Forecasting (...
This study demonstrates how Structural Topic Modeling (STM) can be used to analyze qualitative student comments in conjunction with quantitative student evaluation of teaching (SET) scores, providing a scalable framework for interpreting student eval...
This study investigates the evolution of online public opinion during the COVID-19 pandemic by integrating topic mining with sentiment analysis. To overcome the limitations of traditional short-text models and improve the accuracy of sentiment detect...
To support evidence-based precision medicine and clinical decision-making, we need to identify accurate, appropriate, and clinically relevant studies from voluminous biomedical literature. To address the issue of accurate identification of high impac...
Online misogyny is a significant societal challenge that reinforces gender inequalities and discourages women from engaging fully in digital spaces. Traditional moderation methods often fail to address the dynamic and context-dependent nature of miso...
This study presents a comparative analysis of four topic modeling techniques —Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), Probabilistic Latent Semantic Analysis (pLSA), and Non-negative Mat...
Topic extraction and evolution analysis became a research hotspot in the academic community due to its ability to reveal the development trend of a certain field and discover the evolution law of topic content in different development stages of the f...
This article focuses on the role of information disorders in media coverage of cancer as a growing public health problem on both sides of the Atlantic. Taking the examples of Chile and Spain, we analysed news (n = 5522) published by major digital new...
Periodontics is a complex field characterized by a constantly growing body of research, which poses a challenge for researchers and stakeholders striving to stay abreast of the evolving literature. Traditional bibliometric surveys, while accurate, ar...
The hallmarks of cancer represent an essential concept for discovering novel knowledge about cancer and for extracting the complexity of cancer. Due to the lack of topic analysis frameworks optimized specifically for cancer data, the studies on topic...
Document-level Sentiment Analysis is a complex task that implies the analysis of large textual content that can incorporate multiple contradictory polarities at the phrase and word levels. Most of the current approaches either represent textual data...
As the next frontier in wireless communication, the landscape of 6G technologies is characterized by its rapid evolution and increasing complexity, driven by the need to address global challenges such as ubiquitous connectivity, ultra-high data rates...
Topic modeling is a fundamental technique in natural language processing used to uncover latent themes in large text corpora, yet existing approaches struggle to jointly achieve interpretability, semantic coherence, and scalability. Classical probabi...
Text classification is a process of classifying textual contents to a set of predefined classes and categories. As enormous numbers of documents and contextual contents are introduced every day on the Internet, it becomes essential to use text classi...
Paraphrase identification has been one of the major topics in Natural Language Processing (NLP). However, how to interpret a diversity of contexts such as lexical and semantic information within a sentence as relevant features is still an open proble...
Hazards at construction sites can lead to severe accidents, posing significant risks to worker safety, financial stability, and public confidence in industry safety standards. As a result, understanding and preventing these accidents has become incre...
As the construction of smart grids is in full swing, the number of secondary equipment is also increasing, resulting in an explosive growth of power big data, which is related to the safe and stable operation of power systems. During the operation of...
This study presents a novel approach to examining the psychological impact of emerging technologies through the development of a Hype Cycle Model (HCM), utilizing sentiment analysis and topic modeling. Focusing on electric vehicles, we investigate ho...
Despite the significance and growth of wind energy as a major source of renewable energy, research on the risks of wind turbines in the form of accidents and failures has attracted limited attention. Research that applies data analytics methodologica...
Low-light image enhancement has been gradually becoming a hot research topic in recent years due to its wide usage as an important pre-processing step in computer vision tasks. Although numerous methods have achieved promising results, some of them s...
Cyber Threat Intelligence (CTI) reports are essential resources for identifying the Tactics, Techniques, and Procedures (TTPs) of hackers and cyber threat actors. However, these reports are often lengthy and unstructured, which limits their suitabili...
Sarcasm is a sophisticated figurative language that is prevalent on social media platforms. Automatic sarcasm detection is significant for understanding the real sentiment tendencies of users. Traditional approaches mostly focus on content features b...
The proliferation of fake news poses a significant challenge in today’s information landscape, spanning diverse domains and topics and undermining traditional detection methods confined to specific domains. In response, there is a growing inter...
The accelerating pace of digital life has intensified psychological strain, increasing the demand for accessible and systematized emotional support tools. Relaxing video games—defined as low-pressure, non-competitive games designed to promote c...
This paper proposes a multi-level max-margin discriminative analysis (M3DA) framework, which takes both coarse and fine semantics into consideration, for the annotation of high-resolution satellite images. In order to generate more discriminative top...
Water infrastructure integrity, quality, and distribution are fundamental for public health, environmental sustainability, economic development, and climate change resilience. Ensuring the robustness and quality of water infrastructure is pivotal for...
This paper presents an original methodology for planar development of a frescoed dome with an oval plan. Input data include a rigorous geometric survey, performed with a laser scanner, and a photogrammetry campaign, which associates a high-quality ph...
During multi-turn dialogue, with the increase in dialogue turns, the difficulty of intention recognition and the generation of the following sentence reply become more and more difficult. This paper mainly optimizes the context information extraction...
This study investigates semantic text segmentation enhanced by large language model (LLM) embeddings. We assess how effectively embeddings capture semantic coherence and topic closure by integrating them into both classical clustering algorithms and...
Education for the environment and sustainable development is the one important thing for being studied. At formal school in Indonesia, it was integrated into the subject matter like social science, natural science, geography, or biology. The study ab...
The review of state of the art on creating and managing learning resources and accessible Open Educational Resources (OER) and Massive Open Online Courses (MOOC) is a topic that cannot only consider formal literature. The evidence and lack of a measu...
Here, we present a systematic review of the literature on Early Leaving from Education and Training (ELET), which uses the life course paradigm as an explanatory model or approach. This review has returned little in the way of scientific literature,...
In the era of big data, a large amount of unstructured text data springs up every day. Entity linking involves relating the mentions found in the texts to the corresponding entities, which stand for objective things in the real world, in a knowledge...
Software defect prediction (SDP) methodology could enhance software’s reliability through predicting any suspicious defects in its source code. However, developing defect prediction models is a difficult task, as has been demonstrated recently....
Large language models, such as ChatGPT-4 and Google Bard, have demonstrated potential in healthcare. This study explores their utility in occupational medicine, a field where decisions rely on compliance with specific workplace health and safety regu...
Combinatorial fusion algorithm (CFA) is a machine learning and artificial intelligence (ML/AI) framework for combining multiple scoring systems using the rank-score characteristic (RSC) function and cognitive diversity (CD). When measuring the releva...
23 November 2021
The 2020 global outbreak of COVID-19 exposed and heightened threats to mental health across societies. Research has indicated that individuals with chronic physical health conditions are at high risk for suffering from severe COVID-19 illness and fro...
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