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  • Open Access

    ARTICLE

    Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

    Muhammad Rizwan Rashid Rana1,*, Saif Ur Rehman1, Asif Nawaz1, Tariq Ali1, Azhar Imran2, Abdulkareem Alzahrani3, Abdullah Almuhaimeed4,*

    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 >

  • Open Access

    ARTICLE

    Topic Modelling and Sentimental Analysis of Students’ Reviews

    Omer S. Alkhnbashi1, Rasheed Mohammad Nassr2,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6835-6848, 2023, DOI:10.32604/cmc.2023.034987 - 28 December 2022

    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… More >

  • Open Access

    ARTICLE

    Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

    Ernesto Lee1, Furqan Rustam2, Hina Fatima Shahzad2, Patrick Bernard Washington3, Abid Ishaq3, Imran Ashraf4,*

    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 >

  • Open Access

    ARTICLE

    Aspect Level Songs Rating Based Upon Reviews in English

    Muhammad Aasim Qureshi1, Muhammad Asif2, Saira Anwar3, Umar Shaukat1, Atta-ur-Rahman4, Muhammad Adnan Khan5,*, Amir Mosavi6,7,8

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2589-2605, 2023, DOI:10.32604/cmc.2023.032173 - 31 October 2022

    Abstract With the advancements in internet facilities, people are more inclined towards the use of online services. The service providers shelve their items for e-users. These users post their feedbacks, reviews, ratings, etc. after the use of the item. The enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these items. Sentiment Analysis (SA) is a technique that performs such decision analysis. This research targets the ranking and rating through sentiment analysis of these reviews, on different aspects. As a case study, Songs are opted to More >

  • Open Access

    ARTICLE

    Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

    K. Selvasheela1,*, A. M. Abirami2, Abdul Khader Askarunisa3

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2537-2552, 2023, DOI:10.32604/csse.2023.029148 - 01 August 2022

    Abstract Nowadays, commercial transactions and customer reviews are part of human life and various business applications. The technologies create a great impact on online user reviews and activities, affecting the business process. Customer reviews and ratings are more helpful to the new customer to purchase the product, but the fake reviews completely affect the business. The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information. Therefore, in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity. Here, Amazon Product Kaggle dataset information is utilized More >

  • Open Access

    ARTICLE

    Deep Learning Enabled Social Media Recommendation Based on User Comments

    K. Saraswathi1,*, V. Mohanraj2, Y. Suresh2, J. Senthilkumar2

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1691-1702, 2023, DOI:10.32604/csse.2023.027987 - 15 June 2022

    Abstract Nowadays, review systems have been developed with social media Recommendation systems (RS). Although research on RS social media is increasing year by year, the comprehensive literature review and classification of this RS research is limited and needs to be improved. The previous method did not find any user reviews within a time, so it gets poor accuracy and doesn’t filter the irrelevant comments efficiently. The Recursive Neural Network-based Trust Recommender System (RNN-TRS) is proposed to overcome this method’s problem. So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately. The… More >

  • Open Access

    ARTICLE

    Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

    Mubashar Hussain1, Toqir A. Rana2,3, Aksam Iftikhar4, M. Usman Ashraf5,*, Muhammad Waseem Iqbal6, Ahmed Alshaflut7, Abdullah Alourani8

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4641-4656, 2022, DOI:10.32604/cmc.2022.024759 - 28 July 2022

    Abstract In the field of sentiment analysis, extracting aspects or opinion targets from user reviews about a product is a key task. Extracting the polarity of an opinion is much more useful if we also know the targeted Aspect or Feature. Rule based approaches, like dependency-based rules, are quite popular and effective for this purpose. However, they are heavily dependent on the authenticity of the employed parts-of-speech (POS) tagger and dependency parser. Another popular rule based approach is to use sequential rules, wherein the rules formulated by learning from the user’s behavior. However, in general, the… More >

  • Open Access

    ARTICLE

    Sentiment Analysis of Roman Urdu on E-Commerce Reviews Using Machine Learning

    Bilal Chandio1, Asadullah Shaikh2, Maheen Bakhtyar1, Mesfer Alrizq2, Junaid Baber1, Adel Sulaiman2,*, Adel Rajab2, Waheed Noor3

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1263-1287, 2022, DOI:10.32604/cmes.2022.019535 - 19 April 2022

    Abstract Sentiment analysis task has widely been studied for various languages such as English and French. However, Roman Urdu sentiment analysis yet requires more attention from peer-researchers due to the lack of Off-the-Shelf Natural Language Processing (NLP) solutions. The primary objective of this study is to investigate the diverse machine learning methods for the sentiment analysis of Roman Urdu data which is very informal in nature and needs to be lexically normalized. To mitigate this challenge, we propose a fine-tuned Support Vector Machine (SVM) powered by Roman Urdu Stemmer. In our proposed scheme, the corpus data… More >

  • Open Access

    ARTICLE

    Detecting and Analysing Fake Opinions Using Artificial Intelligence Algorithms

    Mosleh Hmoud Al-Adhaileh1, Fawaz Waselallah Alsaade2,*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 643-655, 2022, DOI:10.32604/iasc.2022.021225 - 26 October 2021

    Abstract In e-commerce and on social media, identifying fake opinions has become a tremendous challenge. Such opinions are widely generated on the internet by fake viewers, also called fraudsters. They write deceptive reviews that purport to reflect actual user experience either to promote some products or to defame others. They also target the reputations of e-businesses. Their aim is to mislead customers to make a wrong purchase decision by selecting undesired products. Such reviewers are often paid by rival e-business companies to compose positive reviews of their products and/or negative reviews of other companies’ products. The… More >

  • Open Access

    ARTICLE

    A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

    Muhammad Aasim Qureshi1,*, Muhammad Asif1, Mohd Fadzil Hassan2, Ghulam Mustafa1, Muhammad Khurram Ehsan1, Aasim Ali1, Unaza Sajid1

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4987-5004, 2022, DOI:10.32604/cmc.2022.020544 - 11 October 2021

    Abstract In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of… More >

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