Elias Hossain¹, Abdullah Alshahrani², Wahidur Rahman^3,*

Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 109-123, 2023, DOI:10.32604/iasc.2022.029511

Abstract This paper aims to develop Machine Learning algorithms to classify electronic articles related to this phenomenon by retrieving information and topic modelling. The Methodology of this study is categorized into three phases: the Text Classification Approach (TCA), the Proposed Algorithms Interpretation (PAI), and finally, Information Retrieval Approach (IRA). The TCA reflects the text preprocessing pipeline called a clean corpus. The Global Vectors for Word Representation (Glove) pre-trained model, FastText, Term Frequency-Inverse Document Frequency (TF-IDF), and Bag-of-Words (BOW) for extracting the features have been interpreted in this research. The PAI manifests the Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network… More >

Hsu-Hua Lee, Minh T. N. Nguyen^*

Journal of New Media, Vol.5, No.1, pp. 65-80, 2023, DOI:10.32604/jnm.2023.045792

Abstract YouTube videos on sustainable fashion enable the public to gain basic knowledge about this concept. In this paper, we analyse user comments on YouTube videos that contain sustainable fashion content. The paper’s main objective is to help content creators and business managers effectively understand the perspectives of viewers, thus improving video quality and developing business. We analysed a dataset of 17,357 comments collected from 15 sustainable fashion YouTube videos. First, we use Latent Dirichlet Allocation (LDA), a topic modelling technique, to discover the abstract topics. In addition, we use two approaches to rank these topics: ranking based on proportion and… More >

Haein Lee¹, Seon Hong Lee¹, Kyeo Re Lee², Jang Hyun Kim^3,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 6023-6037, 2023, DOI:10.32604/cmc.2023.039104

Abstract Environmental, social, and governance (ESG) factors are critical in achieving sustainability in business management and are used as values aiming to enhance corporate value. Recently, non-financial indicators have been considered as important for the actual valuation of corporations, thus analyzing natural language data related to ESG is essential. Several previous studies limited their focus to specific countries or have not used big data. Past methodologies are insufficient for obtaining potential insights into the best practices to leverage ESG. To address this problem, in this study, the authors used data from two platforms: LexisNexis, a platform that provides media monitoring, and… More >

Shabir Hussain¹, Muhammad Ayoub², Yang Yu¹, Junaid Abdul Wahid¹, Akmal Khan³, Dietmar P. F. Moller⁴, Hou Weiyan^1,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5355-5377, 2023, DOI:10.32604/cmc.2023.036779

Abstract As the COVID-19 pandemic swept the globe, social media platforms became an essential source of information and communication for many. International students, particularly, turned to Twitter to express their struggles and hardships during this difficult time. To better understand the sentiments and experiences of these international students, we developed the Situational Aspect-Based Annotation and Classification (SABAC) text mining framework. This framework uses a three-layer approach, combining baseline Deep Learning (DL) models with Machine Learning (ML) models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset. Using the proposed aspect2class annotation algorithm, we… More >

Yun-Jung Choi¹, Youn-Joo Um^2,*

International Journal of Mental Health Promotion, Vol.25, No.3, pp. 421-431, 2023, DOI:10.32604/ijmhp.2023.023255

Abstract Major media outlets have run many articles on the COVID-19 pandemic. Since the public suffers cognitive and emotional effects related to COVID-19 from such reports, we analyzed and reviewed the topics of news reports. We searched newspaper articles with the term ‘COVID-19’ term in four Korean daily newspapers from January 20, 2020, when the first patient in Korea was found, to June 15, 2020. Topic modeling analysis was conducted through text mining using R. Five themes were found: “Changes in people’s everyday life,” “Socio-economic shock,” “Trends in infection,” “Role of the government and business,” and “Increased psychological anxiety,” which all… More >

Omer S. Alkhnbashi¹, Rasheed Mohammad Nassr^2,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6835-6848, 2023, DOI:10.32604/cmc.2023.034987

Abstract Globally, educational institutions have reported a dramatic shift to online learning in an effort to contain the COVID-19 pandemic. The fundamental concern has been the continuance of education. As a result, several novel solutions have been developed to address technical and pedagogical issues. However, these were not the only difficulties that students faced. The implemented solutions involved the operation of the educational process with less regard for students’ changing circumstances, which obliged them to study from home. Students should be asked to provide a full list of their concerns. As a result, student reflections, including those from Saudi Arabia, have… More >

K. Anuratha^1,*, M. Parvathy²

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3005-3021, 2023, DOI:10.32604/csse.2023.031553

Abstract The COVID-19 pandemic has become one of the severe diseases in recent years. As it majorly affects the common livelihood of people across the universe, it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak. The public opinions are been shared enormously in microblogging media like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics, sports, entertainment etc., This work presents a combination of Intensity Based Emotion Classification Convolution Neural Network… More >

Sagheer Abbas¹, Syed Ali Raza^1,2, M. A. Khan³, Muhammad Adnan Khan^4,*, Atta-ur-Rahman⁵, Kiran Sultan⁶, Amir Mosavi^7,8,9

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3263-3278, 2023, DOI:10.32604/cmc.2023.032864

Abstract File labeling techniques have a long history in analyzing the anthological trends in computational linguistics. The situation becomes worse in the case of files downloaded into systems from the Internet. Currently, most users either have to change file names manually or leave a meaningless name of the files, which increases the time to search required files and results in redundancy and duplications of user files. Currently, no significant work is done on automated file labeling during the organization of heterogeneous user files. A few attempts have been made in topic modeling. However, one major drawback of current topic modeling approaches… More >

Mohamed Ali Rakrouki^1,*, Nawaf Alharbe¹, Mashael Khayyat², Abeer Aljohani¹

Computer Systems Science and Engineering, Vol.45, No.1, pp. 395-408, 2023, DOI:10.32604/csse.2023.029032

Abstract Recently, automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization. However, most of the computing methods that are used in real systems are based on graph models, which are characterized by their simplicity and stability. Thus, this paper proposes an improved extractive text summarization algorithm based on both topic and graph models. The methodology of this work consists of two stages. First, the well-known TextRank algorithm is analyzed and its shortcomings are investigated. Then, an improved method is proposed with a new computational model of sentence weights.… More >

Jang Hyun Kim^1,2,3, Jisung Jang^1,3, Yonghwan Kim⁴, Dongyan Nan^1,2,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3815-3826, 2022, DOI:10.32604/cmc.2022.029507

Abstract This study explored user satisfaction with mobile payments by applying a novel structural topic model. Specifically, we collected 17,927 online reviews of a specific mobile payment (i.e., PayPal). Then, we employed a structural topic model to investigate the relationship between the attributes extracted from online reviews and user satisfaction with mobile payment. Consequently, we discovered that “lack of reliability” and “poor customer service” tend to appear in negative reviews. Whereas, the terms “convenience,” “user-friendly interface,” “simple process,” and “secure system” tend to appear in positive reviews. On the basis of information system success theory, we categorized the topics “convenience,” “user-friendly… More >