Table of Content

Advance Machine Learning for Sentiment Analysis over Various Domains and Applications

Submission Deadline: 30 June 2024 Submit to Special Issue

Guest Editors

Dr. Muhammad Asif, National Textile University, Pakistan.
Dr. Kemal Polat, Bolu Abant Izzet Baysal University, Turkey.
Dr. Osama Sohaib, American University of Ras Al Khaimah, UAE.


Dear Colleagues, nowadays due to the massive amount of digital data on different social platforms it has become very necessary to closely monitor that data produced in terms of tweets, comments, blogs writing, etc. Many countries and important organizations like defense, security, advertising agencies, feedback on different products, the popularity of politicians, extremism detection need state of the art sentiment analysis methods to uncover the hidden factors from these sentiments of people. This special issue will focus on the potential state of the art methods and techniques to uncover the patterns and knowledge from the tweets, comments and blogs. The main target of this special issue will be to present the research articles related to deep learning, transfer learning, RNN based models, CNN-based models, Hybrid neural network models, techniques in Natural Language Processing (NLP) and related areas. The issue will accept the papers related to the following areas.


Machine learning for sentiment analysis.

NLP for sentiment analysis.

Sentiment analysis for extremisms detection.

Sentiment analysis for product reviews.

Sentiment analysis for threat detections.

Multi-domain Sentiment Classification.

Aspect-based sentiment analysis.

Deep learning for sentiment analysis.

NN-based approach for sentiment analysis.

Syntax-Aware Aspect-Term Sentiment Analysis.

Graph-based methods.

Visualization of the sentiment aspects.

Span-Based Models for SA.

Real-Time Sentiment Analysis

Any related methods and techniques for uncovering the facts from sentiments.


Machine learning; sentiment analysis; NLP; extremisms detection; product reviews.

Published Papers

  • Open Access


    A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

    Bishrul Haq, Sher Muhammad Daudpota, Ali Shariq Imran, Zenun Kastrati, Waheed Noor
    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 115-137, 2023, DOI:10.32604/cmc.2023.040638
    (This article belongs to this Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract The Internet has become one of the significant sources for sharing information and expressing users’ opinions about products and their interests with the associated aspects. It is essential to learn about product reviews; however, to react to such reviews, extracting aspects of the entity to which these reviews belong is equally important. Aspect-based Sentiment Analysis (ABSA) refers to aspects extracted from an opinionated text. The literature proposes different approaches for ABSA; however, most research is focused on supervised approaches, which require labeled datasets with manual sentiment polarity labeling and aspect tagging. This study proposes a semi-supervised approach with minimal human… More >

  • Open Access


    Push-Based Content Dissemination and Machine Learning-Oriented Illusion Attack Detection in Vehicular Named Data Networking

    Arif Hussain Magsi, Ghulam Muhammad, Sajida Karim, Saifullah Memon, Zulfiqar Ali
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3131-3150, 2023, DOI:10.32604/cmc.2023.040784
    (This article belongs to this Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Recent advancements in the Vehicular Ad-hoc Network (VANET) have tremendously addressed road-related challenges. Specifically, Named Data Networking (NDN) in VANET has emerged as a vital technology due to its outstanding features. However, the NDN communication framework fails to address two important issues. The current NDN employs a pull-based content retrieval network, which is inefficient in disseminating crucial content in Vehicular Named Data Networking (VNDN). Additionally, VNDN is vulnerable to illusion attackers due to the administrative-less network of autonomous vehicles. Although various solutions have been proposed for detecting vehicles’ behavior, they inadequately addressed the challenges specific to VNDN. To deal with… More >

  • Open Access


    Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

    Asif Khan, Huaping Zhang, Nada Boudjellal, Arshad Ahmad, Maqbool Khan
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3345-3361, 2023, DOI:10.32604/cmc.2023.041520
    (This article belongs to this Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various topics. In recent years, the rise of social media platforms (SMPs) has provided a rich source of data for analyzing public opinions, particularly in the context of election-related conversations. Nevertheless, sentiment analysis of election-related tweets presents unique challenges due to the complex language used, including figurative expressions, sarcasm, and the spread of misinformation. To address these challenges, this paper proposes Election-focused Bidirectional Encoder Representations from Transformers (ElecBERT), a new model for sentiment analysis in the context of election-related tweets. Election-related tweets pose unique challenges for sentiment… More >

  • Open Access


    Text Augmentation-Based Model for Emotion Recognition Using Transformers

    Fida Mohammad, Mukhtaj Khan, Safdar Nawaz Khan Marwat, Naveed Jan, Neelam Gohar, Muhammad Bilal, Amal Al-Rasheed
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3523-3547, 2023, DOI:10.32604/cmc.2023.040202
    (This article belongs to this Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Emotion Recognition in Conversations (ERC) is fundamental in creating emotionally intelligent machines. Graph-Based Network (GBN) models have gained popularity in detecting conversational contexts for ERC tasks. However, their limited ability to collect and acquire contextual information hinders their effectiveness. We propose a Text Augmentation-based computational model for recognizing emotions using transformers (TA-MERT) to address this. The proposed model uses the Multimodal Emotion Lines Dataset (MELD), which ensures a balanced representation for recognizing human emotions. The model used text augmentation techniques to produce more training data, improving the proposed model’s accuracy. Transformer encoders train the deep neural network (DNN) model, especially… More >

  • Open Access


    Analyzing Arabic Twitter-Based Patient Experience Sentiments Using Multi-Dialect Arabic Bidirectional Encoder Representations from Transformers

    Sarab AlMuhaideb, Yasmeen AlNegheimish, Taif AlOmar, Reem AlSabti, Maha AlKathery, Ghala AlOlyyan
    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 195-220, 2023, DOI:10.32604/cmc.2023.038368
    (This article belongs to this Special Issue: Advance Machine Learning for Sentiment Analysis over Various Domains and Applications)
    Abstract Healthcare organizations rely on patients’ feedback and experiences to evaluate their performance and services, thereby allowing such organizations to improve inadequate services and address any shortcomings. According to the literature, social networks and particularly Twitter are effective platforms for gathering public opinions. Moreover, recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors, including healthcare. The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus. The authors collected 12,400 tweets from Arabic patients discussing patient experiences related to… More >

Share Link