Mohemmed Sha^*

Computer Systems Science and Engineering, Vol.45, No.1, pp. 857-868, 2023, DOI:10.32604/csse.2023.031321

Abstract The use of Amazon Web Services is growing rapidly as more users are adopting the technology. It has various functionalities that can be used by large corporates and individuals as well. Sentiment analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related emotions. In this research work, sentiment analysis is performed on the AWS Elastic Compute Cloud (EC2) through Twitter data. The data is managed to the EC2 by using elastic load balancing. The collected data is subjected to preprocessing approaches to clean the data, and then machine… More >

Saud S. Alotaibi¹, Eatedal Alabdulkreem², Sami Althahabi³, Manar Ahmed Hamza^4,*, Mohammed Rizwanullah⁴, Abu Sarwar Zamani⁴, Abdelwahed Motwakel⁴, Radwa Marzouk⁵

Computer Systems Science and Engineering, Vol.45, No.1, pp. 737-751, 2023, DOI:10.32604/csse.2023.030170

Abstract Sentiment analysis or opinion mining (OM) concepts become familiar due to advances in networking technologies and social media. Recently, massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult. Since OM find useful in business sectors to improve the quality of the product as well as services, machine learning (ML) and deep learning (DL) models can be considered into account. Besides, the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process. Therefore, in this paper, a new Artificial Fish Swarm Optimization with Bidirectional Long… More >

Samuel Kofi Akpatsa^1,*, Hang Lei¹, Xiaoyu Li¹, Victor-Hillary Kofi Setornyo Obeng¹, Ezekiel Mensah Martey¹, Prince Clement Addo², Duncan Dodzi Fiawoo³

Journal of Quantum Computing, Vol.4, No.1, pp. 1-11, 2022, DOI:10.32604/jqc.2022.026658

Abstract The ability of pre-trained BERT model to achieve outstanding performances on many Natural Language Processing (NLP) tasks has attracted the attention of researchers in recent times. However, the huge computational and memory requirements have hampered its widespread deployment on devices with limited resources. The concept of knowledge distillation has shown to produce smaller and faster distilled models with less trainable parameters and intended for resource-constrained environments. The distilled models can be fine-tuned with great performance on a wider range of tasks, such as sentiment classification. This paper evaluates the performance of DistilBERT model and other pre-canned text classifiers on a… More >

Abdul Rahaman Wahab Sait^1,*, Mohamad Khairi Ishak²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2553-2567, 2023, DOI:10.32604/csse.2023.029603

Abstract Sentiment analysis (SA) is the procedure of recognizing the emotions related to the data that exist in social networking. The existence of sarcasm in textual data is a major challenge in the efficiency of the SA. Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection, punctuations, and sentiment shift that are vital indicators of sarcasm. With the advent of deep-learning, recent works, leveraging neural networks in learning lexical and contextual features, removing the need for handcrafted feature. In this aspect, this study designs a deep learning with natural language processing enabled SA (DLNLP-SA)… More >

C. P. Thamil Selvi^1,*, R. PushpaLakshmi²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2439-2456, 2023, DOI:10.32604/csse.2023.029254

Abstract One of the drastically growing and emerging research areas used in most information technology industries is Bigdata analytics. Bigdata is created from social websites like Facebook, WhatsApp, Twitter, etc. Opinions about products, persons, initiatives, political issues, research achievements, and entertainment are discussed on social websites. The unique data analytics method cannot be applied to various social websites since the data formats are different. Several approaches, techniques, and tools have been used for big data analytics, opinion mining, or sentiment analysis, but the accuracy is yet to be improved. The proposed work is motivated to do sentiment analysis on Twitter data… More >

Mahesh B. Shelke¹, Jeong Gon Lee^2,*, Sovan Samanta³, Sachin N. Deshmukh¹, G. Bhalke Daulappa⁴, Rahul B. Mannade⁵, Arun Kumar Sivaraman⁶

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2457-2468, 2023, DOI:10.32604/csse.2023.027979

Abstract In today’s digital world, millions of individuals are linked to one another via the Internet and social media. This opens up new avenues for information exchange with others. Sentiment analysis (SA) has gotten a lot of attention during the last decade. We analyse the challenges of Sentiment Analysis (SA) in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which we first produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter. We also choose domain experts to manually annotate Marathi microblogging posts with positive, negative,… More >

Hanan M. Alghamdi¹, Saadia H.A. Hamza², Aisha M. Mashraqi³, Sayed Abdel-Khalek^4,5,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5985-5999, 2022, DOI:10.32604/cmc.2022.031732

Abstract World Wide Web enables its users to connect among themselves through social networks, forums, review sites, and blogs and these interactions produce huge volumes of data in various forms such as emotions, sentiments, views, etc. Sentiment Analysis (SA) is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive, negative, and neutral. However, Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing (NLP). Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array… More >

M. Mohamed Iqbal^1,*, K. Latha²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1831-1842, 2023, DOI:10.32604/iasc.2023.029036

Abstract The public is increasingly using social media platforms such as Twitter and Facebook to express their views on a variety of topics. As a result, social media has emerged as the most effective and largest open source for obtaining public opinion. Single node computational methods are inefficient for sentiment analysis on such large datasets. Supercomputers or parallel or distributed processing are two options for dealing with such large amounts of data. Most parallel programming frameworks, such as MPI (Message Processing Interface), are difficult to use and scale in environments where supercomputers are expensive. Using the Apache Spark Parallel Model, this… More >

Ayman Mohamed Mostafa^*

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

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, most research methodologies analyze datasets… More >

R. Bhaskaran^1,*, S. Saravanan¹, M. Kavitha², C. Jeyalakshmi³, Seifedine Kadry⁴, Hafiz Tayyab Rauf⁵, Reem Alkhammash⁶

Computer Systems Science and Engineering, Vol.44, No.1, pp. 235-247, 2023, DOI:10.32604/csse.2023.024399

Abstract Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover,… More >