Zohaib Ahmad Khan¹, Yuanqing Xia^1,*, Ahmed Khan², Muhammad Sadiq², Mahmood Alam³, Fuad A. Awwad⁴, Emad A. A. Ismail⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2771-2793, 2024, DOI:10.32604/cmc.2024.046897 - 15 May 2024

Abstract Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significant source of user-generated content. The development of sentiment lexicons that can support languages other than English is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existing sentiment analysis systems focus on English, leaving a significant research gap in other languages due to limited resources and tools. This research aims to address this gap by building a sentiment lexicon for local languages, which is then used with a machine learning algorithm for efficient sentiment analysis.… More >

Jing Yang, Bin Ji, Shasha Li^*, Jun Ma, Jie Yu

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4303-4315, 2024, DOI:10.32604/cmc.2023.035594 - 26 March 2024

Abstract Named entity recognition (NER) is a fundamental task of information extraction (IE), and it has attracted considerable research attention in recent years. The abundant annotated English NER datasets have significantly promoted the NER research in the English field. By contrast, much fewer efforts are made to the Chinese NER research, especially in the scientific domain, due to the scarcity of Chinese NER datasets. To alleviate this problem, we present a Chinese scientific NER dataset–SciCN, which contains entity annotations of titles and abstracts derived from 3,500 scientific papers. We manually annotate a total of 62,059 entities,… More >

Wadhah Mohammed M. Aqlan¹, Ghassan Ahmed Ali^2,*, Khairan Rajab², Adel Rajab², Asadullah Shaikh², Fekry Olayah², Shehab Abdulhabib Saeed Alzaeemi^3,*, Kim Gaik Tay³, Mohd Adib Omar¹, Ernest Mangantig⁴

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 665-686, 2023, DOI:10.32604/cmc.2023.039228 - 08 June 2023

Abstract Thalassemia syndrome is a genetic blood disorder induced by the reduction of normal hemoglobin production, resulting in a drop in the size of red blood cells. In severe forms, it can lead to death. This genetic disorder has posed a major burden on public health wherein patients with severe thalassemia need periodic therapy of iron chelation and blood transfusion for survival. Therefore, controlling thalassemia is extremely important and is made by promoting screening to the general population, particularly among thalassemia carriers. Today Twitter is one of the most influential social media platforms for sharing opinions… More >

Bashar Tahayna, Ramesh Kumar Ayyasamy^*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 37-54, 2023, DOI:10.32604/csse.2023.036648 - 26 May 2023

Abstract Millions of people are connecting and exchanging information on social media platforms, where interpersonal interactions are constantly being shared. However, due to inaccurate or misleading information about the COVID-19 pandemic, social media platforms became the scene of tense debates between believers and doubters. Healthcare professionals and public health agencies also use social media to inform the public about COVID-19 news and updates. However, they occasionally have trouble managing massive pandemic-related rumors and frauds. One reason is that people share and engage, regardless of the information source, by assuming the content is unquestionably true. On Twitter,… More >

Muhammad Rizwan Rashid Rana^1,*, Saif Ur Rehman¹, Asif Nawaz¹, Tariq Ali¹, Azhar Imran², Abdulkareem Alzahrani³, Abdullah Almuhaimeed^4,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2415-2428, 2023, DOI:10.32604/csse.2023.035149 - 09 February 2023

Abstract People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events, public products and the latest affairs. People share their thoughts and feelings about various topics, including products, news, blogs, etc. In user reviews and tweets, sentiment analysis is used to discover opinions and feelings. Sentiment polarity is a term used to describe how sentiment is represented. Positive, neutral and negative are all examples of it. This area is still in its infancy and needs several critical upgrades. Slang and hidden emotions can detract from the accuracy of traditional… More >

Aisha M. Mashraqi, Hanan T. Halawani^*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2555-2570, 2023, DOI:10.32604/csse.2023.031246 - 09 February 2023

Abstract Sentiment Analysis (SA) is one of the Machine Learning (ML) techniques that has been investigated by several researchers in recent years, especially due to the evolution of novel data collection methods focused on social media. In literature, it has been reported that SA data is created for English language in excess of any other language. It is challenging to perform SA for Arabic Twitter data owing to informal nature and rich morphology of Arabic language. An earlier study conducted upon SA for Arabic Twitter focused mostly on automatic extraction of the features from the text.… More >

Azlinah Mohamed^1,3,*, Zuhaira Muhammad Zain², Hadil Shaiba^2,*, Nazik Alturki², Ghadah Aldehim², Sapiah Sakri², Saiful Farik Mat Yatin¹, Jasni Mohamad Zain¹

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1577-1601, 2023, DOI:10.32604/cmc.2023.034746 - 06 February 2023

Abstract The COVID-19 pandemic has spread globally, resulting in financial instability in many countries and reductions in the per capita gross domestic product. Sentiment analysis is a cost-effective method for acquiring sentiments based on household income loss, as expressed on social media. However, limited research has been conducted in this domain using the LexDeep approach. This study aimed to explore social trend analytics using LexDeep, which is a hybrid sentiment analysis technique, on Twitter to capture the risk of household income loss during the COVID-19 pandemic. First, tweet data were collected using Twint with relevant keywords… More >

Samer Abdulateef Waheeb^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 981-998, 2023, DOI:10.32604/csse.2023.035753 - 20 January 2023

Abstract Automatic extraction of the patient’s health information from the unstructured data concerning the discharge summary remains challenging. Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field. Using a sentiment dictionary and feature engineering, the researchers primarily mine semantic text features. However, choosing and designing features requires a lot of manpower. The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space. A composite model including Active Learning (AL),… More >

Ernesto Lee¹, Furqan Rustam², Hina Fatima Shahzad², Patrick Bernard Washington³, Abid Ishaq³, Imran Ashraf^4,*

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3053-3077, 2023, DOI:10.32604/csse.2023.029059 - 21 December 2022

Abstract With the increasing usage of drugs to remedy different diseases, drug safety has become crucial over the past few years. Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae. Such diversification is both helpful and dangerous as such medicine proves to be more effective or shows side effects to different patients. Despite clinical trials, side effects are reported when the medicine is used by the mass public, of which several such experiences are shared on social media platforms. A system capable of analyzing such… More >

Ayman Mohamed Mostafa^*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1015-1034, 2023, DOI:10.32604/iasc.2023.028041 - 06 June 2022

Abstract Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and subjects. Different methods and techniques have been introduced to analyze sentiments for obtaining high accuracy. The sentiment analysis accuracy depends mainly on supervised and unsupervised mechanisms. Supervised mechanisms are based on machine learning algorithms that achieve moderate or high accuracy but the manual annotation of data is considered a time-consuming process. In unsupervised mechanisms, a lexicon is constructed for storing polarity terms. The accuracy of analyzing data is considered moderate or low if the lexicon contains small terms. In addition,… More >